Natural Resources for Human Health (NRFHH)
Natural Resources for Human Health covers a wide range of natural science disciplines. Such fields can be at the all aspects of works bearing on natural compounds of plants, algae, microbes and animals. View more 1.7 CiteScore 2024 2.0 CiteScore Tracker 2025 0.21 SJR 33% Acceptance Rate Open Access
Preliminary Phytochemical Screening and In Vitro Antioxidant Activity of Ocimum sanctum (Tulsi) Leaf Extract: A Laboratory-Based Study
Background: Oxidative stress, driven by an imbalance between reactive oxygen species and endogenous antioxidant defences, contributes to the pathogenesis of numerous chronic and degenerative disorders. Medicinal plants remain an important, low-cost source of natural antioxidants, and Ocimum sanctum Linn. (Tulsi, holy basil) occupies a revered place in the traditional medicine of South India. Objective: To perform a preliminary qualitative phytochemical screening and to evaluate the in vitro free-radical-scavenging capacity of a hydroalcoholic leaf extract of Ocimum sanctum, and to relate its antioxidant activity to its phenolic and flavonoid content. Methods: This was an experimental in vitro laboratory study conducted on a hydroalcoholic (ethanol–water) leaf extract of O. sanctum. Fifteen extract batches were prepared and screened qualitatively for major phytochemical classes using standard tests. Antioxidant activity was assessed by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging assay across concentrations of 10–100 µg/mL in triplicate, with the median inhibitory concentration (IC50) derived by linear regression. Total phenolic content (TPC) and total flavonoid content (TFC) were determined by the Folin–Ciocalteu and aluminium chloride colorimetric methods, respectively. Data are summarised as mean ± standard deviation. Results: The mean extraction yield was 15.25 ± 2.48% (range 11.14–18.57%). Flavonoids, phenolics and terpenoids were detected in all 15 batches (100%), with alkaloids and carbohydrates (73.3%), saponins (66.7%), tannins (60.0%), glycosides (53.3%) and steroids (46.7%) present at lower frequencies; proteins were trace or absent. DPPH scavenging was concentration-dependent, rising from 8.90% at 10 µg/mL to 79.89% at 100 µg/mL (linear fit R² = 0.9985), yielding an extract IC50 of approximately 62.0 µg/mL. TPC was 99.89 ± 16.58 mg gallic acid equivalents (GAE)/g and TFC was 51.40 ± 9.81 mg quercetin equivalents (QE)/g. Conclusion: The hydroalcoholic leaf extract of O. sanctum is rich in phenolics, flavonoids and terpenoids and exhibits moderate, concentration-dependent antioxidant activity consistent with its high phenolic and flavonoid content. These findings provide laboratory support for the traditional South Indian medicinal use of Tulsi and illustrate a simple, low-cost screening approach.
Comparative Antibacterial Activity of Azadirachta indica (Neem) and Curcuma longa (Turmeric) Hydroalcoholic Extracts Against Common Clinical Isolates: An In Vitro Study
Background: The relentless global rise of antimicrobial resistance (AMR) has renewed scientific interest in medicinal plants as reservoirs of novel antibacterial compounds. In South India, Azadirachta indica (neem) and Curcuma longa (turmeric) are deeply embedded in traditional medicine, yet head-to-head comparisons of their antibacterial potency against contemporary clinical isolates are scarce. Objective: To compare the in vitro antibacterial activity of hydroalcoholic extracts of neem leaf and turmeric rhizome against five common clinical bacterial isolates, and to relate activity to phenolic and flavonoid content. Methods: In this in vitro experimental study, hydroalcoholic (ethanol–water) extracts were prepared from neem leaf and turmeric rhizome. Antibacterial activity was assessed by agar well diffusion at 25, 50 and 100 mg/mL in triplicate against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae and Enterococcus faecalis, with a standard antibiotic as positive control and solvent as negative control. Minimum inhibitory (MIC) and minimum bactericidal (MBC) concentrations were determined by broth microdilution. Total phenolic (TPC) and total flavonoid (TFC) contents were quantified. Data were analysed using mean ± SD, independent t-test and one-way ANOVA (p < 0.05). Results: Both extracts showed concentration-dependent activity. The overall mean zone of inhibition was significantly larger for neem (15.44 ± 4.65 mm) than turmeric (13.13 ± 4.32 mm) (Welch t = 2.44, p = 0.017), while both were far exceeded by the positive control (24.03 mm) and negligible for solvent (0.42 mm). At 100 mg/mL, zones ranged 20.4–21.8 mm (neem) and 17.4–18.9 mm (turmeric), with no significant inter-species difference (neem F = 1.28, p = 0.34; turmeric F = 1.47, p = 0.28). MIC/MBC patterns were organism-dependent: turmeric was more potent against E. coli, whereas neem was more potent against P. aeruginosa and E. faecalis. Neem contained higher TPC (165.91 vs 148.54 mg GAE/g) and TFC (74.84 vs 57.79 mg QE/g). Conclusion: Both hydroalcoholic extracts exhibited meaningful, dose-dependent antibacterial activity against Gram-positive and Gram-negative clinical isolates, with neem generally superior—plausibly linked to its higher phenolic/flavonoid content. These low-cost screening findings support the traditional South Indian use of neem and turmeric and their potential relevance amid escalating AMR.
Qualitative and Quantitative Estimation of Total Phenolic and Flavonoid Content in Aqueous and Ethanolic Extracts of Moringa oleifera Leaves: A Comparative Laboratory Study
Background: Moringa oleifera Lam. (drumstick), a fast-growing tree widely cultivated and consumed across South India, is increasingly valued as a nutraceutical and functional food owing to its dense complement of polyphenolic antioxidants. The efficiency with which these moderately polar phenolics and flavonoids are recovered depends strongly on the polarity of the extraction solvent, yet solvent selection is often made without systematic comparison. Objective: To qualitatively screen the phytochemical classes present in M. oleifera leaf extracts and to quantitatively compare total phenolic content (TPC) and total flavonoid content (TFC) between aqueous and ethanolic extracts under standardised laboratory conditions. Methods: In this laboratory-based comparative phytochemical study, aqueous and ethanolic leaf extracts were prepared and subjected to standard qualitative phytochemical tests. TPC was estimated by the Folin–Ciocalteu assay against a gallic acid calibration curve (mg gallic acid equivalents [GAE]/g extract) and TFC by the aluminium chloride colorimetric assay against a quercetin calibration curve (mg quercetin equivalents [QE]/g extract). Three independent analytical replicates were analysed per extract per assay. Data are expressed as mean ± standard deviation (SD) and compared using independent-samples t-tests, with 95% confidence intervals (CI) and Cohen's d; p < 0.05 was considered significant. Results: Both extracts were qualitatively rich in flavonoids, phenolics, tannins, terpenoids, glycosides and carbohydrates. TPC was substantially higher in the ethanolic extract (125.02 ± 2.80 mg GAE/g) than the aqueous extract (79.92 ± 3.07 mg GAE/g; mean difference 45.10 mg GAE/g; t = 18.797, df = 4, p = 0.001; 95% CI 38.44–51.76; Cohen's d = 15.35). TFC was likewise higher in the ethanolic extract (70.98 ± 0.36 mg QE/g) than the aqueous extract (39.35 ± 2.21 mg QE/g; mean difference 31.63 mg QE/g; t = 24.470, df = 4, p = 0.001; 95% CI 28.04–35.22; Cohen's d = 19.98). Conclusion: Ethanolic extraction recovered approximately 1.6-fold more phenolics and 1.8-fold more flavonoids than aqueous extraction, with very large effect sizes, indicating that ethanol (or hydroethanol) is preferable to water for recovering M. oleifera leaf polyphenols in low-cost South Indian nutraceutical and functional-food applications.
In Vitro Antimicrobial Efficacy of Aloe vera Gel Against Oral and Skin Pathogens: A Cross-Sectional Laboratory Study
Background: The accelerating global crisis of antimicrobial resistance (AMR) has renewed interest in botanical antimicrobials as low-cost adjuncts for the management of common oral and skin infections. Aloe vera (L.) Burm.f. is one of the most widely cultivated and traditionally used medicinal plants of South India, yet quantitative comparative screening of its gel against clinically relevant oral and cutaneous pathogens remains limited. Objective: To evaluate the concentration-dependent in vitro antimicrobial efficacy of Aloe vera leaf gel against five oral and skin pathogens and to determine its bactericidal potential. Methods: In this cross-sectional in vitro laboratory comparative study, Aloe vera gel was tested against Staphylococcus aureus, Streptococcus mutans, Enterococcus faecalis, Escherichia coli and Pseudomonas aeruginosa using agar well diffusion at 25, 50, 75 and 100% v/v in triplicate, alongside a standard-antibiotic positive control and a vehicle negative control. Minimum inhibitory (MIC) and minimum bactericidal (MBC) concentrations were determined by broth dilution. The gel was physicochemical and phytochemicals characterized (pH, viscosity, total phenolic and flavonoid content, dry matter). Data were summarized as mean ± SD; the 100% gel was compared with the negative control using independent-samples t-tests with Cohen's d (p < 0.05). Results: Aloe vera gel produced concentration-dependent inhibition against all five pathogens. At 100% v/v, zones ranged from 19.57 ± 0.67 mm (S. aureus) to 13.27 ± 0.45 mm (P. aeruginosa). Gram-positive organisms were most susceptible and P. aeruginosa least. Activity was bactericidal (MBC:MIC ≤ 2) at achievable concentrations, with MIC values of 25–75% v/v. All 100%-versus-control comparisons were highly significant (p < 0.001) with very large effect sizes (Cohen's d 21.00–78.30). The gel was mildly acidic (pH 4.64) and rich in phenolics (37.75 mg GAE/g) and flavonoids (19.52 mg QE/g). Conclusion: Aloe vera gel demonstrated bactericidal, dose-dependent antimicrobial activity against oral and skin pathogens, below the standard antibiotic but far exceeding the vehicle.
Phytochemical Profiling and Free-Radical Scavenging Potential of Phyllanthus niruri Whole-Plant Extract: A Laboratory Study
Background: Oxidative stress arising from an imbalance between reactive oxygen species and endogenous antioxidant defenses contributes to the pathogenesis of a wide spectrum of chronic non-communicable diseases. Medicinal plants used in the Ayurvedic and Siddha systems of South India represent an abundant and inexpensive reservoir of natural antioxidants. Phyllanthus niruri L. (locally "keezhanelli" or bhumyamalaki) is a widely used ethnomedicinal herb whose whole-plant preparations enjoy a long-standing reputation for hepatoprotective and antioxidant activity. Objective: To characterise the phytochemical composition of a P. niruri whole-plant hydroalcoholic extract and to quantify it’s in vitro free-radical scavenging and ferric-reducing potential relative to ascorbic acid. Methods: A laboratory-based phytochemical and in vitro antioxidant study was performed. The whole plant was subjected to hydro alcoholic extraction, and percentage yield was recorded across six batches. Qualitative phytochemical screening was undertaken using standard generic tests. Total phenolic content (TPC) was measured by the Folin–Ciocalteu method (mg Gallic acid equivalents [GAE]/g) and total flavonoid content (TFC) by the aluminum chloride method (mg quercetin equivalents [QE]/g). Antioxidant activity was evaluated by DPPH, ABTS and FRAP assays across 10–100 µg/mL in triplicate, with ascorbic acid as reference standard. Half-maximal inhibitory concentrations (IC50) were derived by four-parameter logistic regression. Data are expressed as mean ± standard deviation, with p<0.05 considered significant. Results: The mean extraction yield was 12.30 ± 1.12% (dark green-brown, semi-solid extract). Screening revealed abundant flavonoids, phenolics, tannins, terpenoids, glycosides and carbohydrates; steroids were absent. TPC was 119.38 ± 2.00 mg GAE/g and TFC 62.88 ± 5.96 mg QE/g. Radical scavenging was concentration-dependent, reaching 55.33% (DPPH) and 60.75% (ABTS) at 100 µg/mL. Extract IC50 values were 52 µg/mL (DPPH) and 46 µg/mL (ABTS) versus 28 µg/mL for ascorbic acid, and ferric-reducing power rose to 217.13 µmol Fe(II)/g. The extract was consistently less potent than ascorbic acid but showed substantial, reproducible activity. Conclusion: The P. niruri whole-plant extract is rich in phenolic and flavonoids and exhibits moderate-to-high, concentration-dependent free-radical scavenging and ferric-reducing capacity. These findings provide a plausible mechanistic basis for, and low-cost laboratory support of, its traditional South Indian medicinal use.
Effect of Fenugreek Seed Supplementation on Fasting and Postprandial Glucose in Type 2 Diabetes Mellitus: A Single-Group Pre–Post Study
Background: Type 2 diabetes mellitus (T2DM) is a growing public-health challenge in India, and there is sustained interest in low-cost dietary adjuncts to conventional pharmacotherapy. Fenugreek (Trigonella foenum-graecum), known locally as methi or vendhayam, is a culinary staple in South India whose soluble fibre and bioactive constituents have plausible glucose-lowering activity. Objective: To estimate the pre–post change in fasting blood glucose (FBG), postprandial blood glucose (PPBG) and glycated haemoglobin (HbA1c) following 12 weeks of adjunctive fenugreek seed supplementation in adults with T2DM. Methods: In this single-group (single-arm) pre–post interventional study conducted at a tertiary care teaching hospital in South India, 60 adults (aged 35–70 years) with established T2DM on stable oral antidiabetic therapy received fenugreek seed powder 10 g/day in two divided doses for 12 weeks as an adjunct to their existing regimen. FBG, PPBG, HbA1c, weight and body mass index (BMI) were measured before and after the intervention. Paired t-tests, 95% confidence intervals (CI) and Cohen's dz effect sizes were computed. Results: All 60 participants completed the study (mean adherence 90.6%). FBG fell from 160.42 (SD 25.59) to 135.87 (SD 27.42) mg/dL (mean change −24.55 mg/dL; 95% CI −26.65 to −22.46; p<0.001; dz=−3.03). PPBG fell from 234.33 (SD 34.61) to 197.25 (SD 35.28) mg/dL (mean change −37.08 mg/dL; 95% CI −40.17 to −33.99; p<0.001; dz=−3.10). HbA1c fell from 8.12 (SD 0.73) to 7.54 (SD 0.76) % (mean change −0.59 percentage points; 95% CI −0.63 to −0.54; p<0.001; dz=−3.32). Weight and BMI decreased modestly (−1.40 kg; −0.51 kg/m²). Only mild adverse effects occurred (8/60, 13.3%). Conclusion: Twelve weeks of adjunctive fenugreek 10 g/day was associated with clinically and statistically significant reductions in FBG, PPBG and HbA1c, with good tolerability, in this South Indian sample. Because the single-arm design has no control group, these associations cannot be attributed to fenugreek alone.
Comparative Nutritional and Phytochemical Evaluation of Cold-Pressed Versus Refined Coconut Oil: A Laboratory Study
Background: Coconut oil is a dietary and cultural staple across the South Indian coconut belt spanning Kerala, Tamil Nadu, and Karnataka. Cold-pressed (virgin) and industrially refined coconut oils are both widely marketed, yet consumers in South India have limited comparative information on how processing influences the retention of bioactive micronutrients, antioxidant capacity, and oxidative stability while preserving basic food-grade quality. Objective: To compare the physicochemical quality, nutritional and phytochemical composition, fatty-acid profile, and antioxidant properties of cold-pressed and refined coconut oil using standard laboratory methods. Methods: In this laboratory-based comparative study, six analytical batches each of cold-pressed and refined coconut oil (n = 12 total) were evaluated for moisture, free fatty acid (FFA), peroxide value, iodine value, and saponification value; for vitamin E, total carotenoids, total phenolics (Folin-Ciocalteu), DPPH radical-scavenging activity, and oxidative stability (Rancimat); and for fatty-acid composition by gas chromatography. Data are expressed as mean ± standard deviation and compared using independent-samples t-tests (df = 10) with Cohen's d effect sizes; p < 0.05 was significant. Results: Cold-pressed oil retained markedly higher vitamin E (48.31 ± 2.81 vs 20.06 ± 3.35 mg/kg), total carotenoids (2.757 ± 0.480 vs 0.768 ± 0.176 mg/kg), and total phenolics (75.42 ± 8.10 vs 26.99 ± 5.03 mg GAE/kg; mean difference 48.43), with correspondingly greater DPPH inhibition (58.93 ± 3.37 vs 22.73 ± 1.90 %) and oxidative stability (18.83 ± 0.63 vs 12.02 ± 0.81 h); all p < 0.001 with very large effect sizes. Refined oil showed lower moisture, FFA, and peroxide value, consistent with purification. Fatty-acid composition was essentially equivalent between oils (both lauric-dominant, ~90-92 % saturated). Conclusion: Cold-pressed coconut oil is nutritionally and antioxidant-superior, whereas refined oil is more purified; fatty-acid composition is equivalent. These low-cost quality indicators are relevant to consumers and the coconut industry across South India.
Effect of Amla (Emblica officinalis) Juice Supplementation on Serum Antioxidant Status in Healthy Volunteers: A Single-Group Pre–Post Study
Background: Oxidative stress, an imbalance between reactive oxygen species and endogenous antioxidant defences, is implicated in the pathogenesis of cardiovascular, metabolic, neurodegenerative and other chronic non-communicable diseases. Amla (Emblica officinalis; syn. Phyllanthus emblica), a fruit deeply embedded in the Ayurvedic and Siddha traditions of South India, is exceptionally rich in vitamin C and hydrolysable-tannin polyphenols and is a plausible low-cost dietary antioxidant. Direct human data on its effect on serum antioxidant status remain limited. Objective: To estimate the change in serum enzymatic and non-enzymatic antioxidant markers and a lipid-peroxidation marker after eight weeks of amla juice supplementation in healthy adults. Methods: In this single-group pre–post interventional study, 50 healthy adult volunteers (31 male, 19 female; mean age 33.4 ± 9.9 years) consumed 30 mL/day of fresh amla juice for 8 weeks. Serum total antioxidant capacity (TAC), superoxide dismutase (SOD), catalase, reduced glutathione (GSH), malondialdehyde (MDA) and vitamin C were measured before and after the intervention by standard methods. Paired t-tests, 95% confidence intervals (CI) and Cohen's dz were computed. Results: Adherence averaged 91.2%. All antioxidant defence markers rose significantly: TAC 1.232→1.472 mmol/L (+0.240; 95% CI 0.221–0.259), SOD 7.871→9.524 U/mL (+1.653; 1.528–1.778), catalase 41.610→49.840 kU/L (+8.230; 7.687–8.773), GSH 4.518→5.303 mg/dL (+0.786; 0.719–0.852) and vitamin C 0.711→0.960 mg/dL (+0.249; 0.225–0.273); all p<0.001. MDA fell from 4.738 to 3.790 nmol/mL (−0.948; 95% CI −1.037 to −0.859; p<0.001), indicating reduced lipid peroxidation. Effect sizes were large (dz 2.96–4.31). Only mild, self-limiting adverse effects occurred (12/50, 24%); there were no serious events and no discontinuations. Conclusion: Eight weeks of amla juice was associated with significantly enhanced enzymatic and non-enzymatic antioxidant defences and reduced lipid peroxidation, consistent with amla's high vitamin C and polyphenol content.
Formulation, Nutritional Analysis and Sensory Evaluation of a Millet- and Legume-Based Health Mix for Elderly Nutrition: A Study
Background: Population ageing in India is accompanied by a high burden of protein–energy undernutrition and micronutrient deficiency among older adults, compounded by poor dentition, reduced appetite and swallowing difficulty. Millets and legumes are inexpensive, locally available, climate-smart foods whose complementary amino acid profiles make them attractive as the basis of an energy- and nutrient-dense health mix suited to elderly nutrition in South India. Objective: To formulate four millet- and legume-based health-mix formulations, to characterise their proximate composition, mineral content and functional properties, and to compare their sensory acceptability among elderly panelists in order to identify the optimal formulation. Methods: Four formulations (F1–F4) combining finger millet, pearl millet, green gram, chickpea, soybean, skim milk powder, flaxseed, jaggery and cardamom were developed and analysed in four batches each by AOAC methods (protein, fat, ash, crude fibre, carbohydrate by difference, energy), with calcium and iron by atomic absorption spectrometry, and functional properties (bulk density, water absorption, reconstitution time, viscosity). Fifty elderly panelists (mean age 70.7 years, SD 6.5) rated each formulation on a 9-point hedonic scale, yielding 200 ratings. Data were summarised as mean ± SD and compared by one-way ANOVA with eta-squared effect sizes (p<0.05). Results: Protein rose from 14.53 g/100 g (F1) to 19.80 g/100 g (F4); crude fibre, calcium (244–273 mg/100 g), iron (5.56–6.96 mg/100 g) and energy increased in parallel as legume and soybean content rose. Formulation F3 achieved the highest overall acceptability (8.08), taste (8.29) and ease of swallowing (8.20) and the highest willingness to consume regularly (84%). Differences across formulations were statistically significant with large effect sizes for overall acceptability (F=32.98, df 3,196, p<0.001, eta²=0.335), taste (F=30.52, p<0.001, eta²=0.318) and ease of swallowing (F=31.53, p<0.001, eta²=0.326). Conclusion: A balanced millet–legume health mix (F3) with moderate finger and pearl millet, higher green gram, chickpea and soybean and reduced jaggery delivered a nutrient-dense, protein- and calcium-rich, easy-to-swallow product with the strongest elderly acceptance, supporting its potential for elderly nutrition in South India.
Effect of Soaked Almond Consumption on Serum Lipid Profile in Young Adults: A Single-Group Pre–Post Interventional Study
Background: Dyslipidemia is emerging early in the life course among Indian adults and is a modifiable driver of premature atherosclerotic cardiovascular disease (CVD). Almonds are some nutrient-dense tree nut rich in monounsaturated fatty acids (MUFA), soluble fibre, plant sterols and vitamin E, and soaking almonds overnight is a widespread traditional dietary practice in South India. Whether a small, culturally familiar daily portion of soaked almonds is associated with measurable improvement in the lipid profile of young adults has not been well characterized in this setting. Objective: To estimate the change in fasting serum lipid profile after 8 weeks of daily soaked almond consumption in young adults from South India. Methods: This was a single-group pre–post interventional study conducted in the [Department of General Medicine/Biochemistry/Nutrition], [name of tertiary care teaching hospital/institution], [city], [state], South India, during [study period]. Sixty apparently healthy young adults aged 18–30 years consumed 20 g of soaked almonds daily for 8 weeks. Fasting total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and non-HDL cholesterol were measured at baseline and week 8 by standard enzymatic methods. Adherence and adverse events were monitored. Paired t-tests, 95% confidence intervals (CI) and Cohen's dz were computed, with p<0.05 considered significant. Results: Participants (mean age 24.5 years; 25 men, 35 women) had a mean adherence of 91.6%. All lipid parameters improved significantly at week 8 (all p<0.001). TC fell from 194.29±25.57 to 178.14±27.34 mg/dL (−16.15 mg/dL; 95% CI −17.58 to −14.72; dz=−2.92), LDL-C from 120.55±18.57 to 107.44±19.30 mg/dL (−13.11 mg/dL; dz=−2.66), TG from 135.15±26.78 to 115.99±28.21 mg/dL (−19.16 mg/dL; dz=−2.74) and non-HDL cholesterol from 146.97±26.06 to 126.82±27.88 mg/dL (−20.15 mg/dL; dz=−3.52), while HDL-C rose from 47.32±7.08 to 51.32±7.04 mg/dL (+4.00 mg/dL; dz=2.18). Body weight (67.3→66.8 kg) and BMI (23.9→23.7 kg/m²) changed minimally. Only mild adverse effects occurred (12/60, 20%); there were no serious events and no discontinuations. Conclusion: Eight weeks of 20 g/day soaked almond consumption was associated with statistically significant and clinically meaningful improvement of the lipid profile in young South Indian adults, with large effect sizes and negligible weight change.
Computational Screening of Andrographis paniculata Constituents Against Dengue Virus NS2B–NS3 Protease: An Integrated In Silico Docking, ADMET, DFT and Molecular-Dynamics Study
Background: Dengue is hyperendemic across South India, yet no licensed direct-acting antiviral exists. The dengue virus (DENV) NS2B–NS3 serine protease, which harbours the catalytic triad His51–Asp75–Ser135 and is essential for viral polyprotein maturation, is a validated antiviral target. Andrographis paniculata (kalmegh; nilavembu), a mainstay of South Indian and Ayurvedic antipyretic practice, is rich in diterpenoid lactones and flavonoids that are attractive scaffolds for computer-aided screening. Objective: To prioritise twelve A. paniculata constituents against the DENV NS2B–NS3 protease through an integrated in silico workflow combining molecular docking, ADMET/drug-likeness profiling, density-functional-theory (DFT) electronic descriptors and molecular dynamics (MD). Methods: Twelve curated constituents (six diterpenoids/lactones, four flavonoids, two phenolic acids) were energy-minimised and docked (five poses per ligand; sixty records) into the protease active site defined by the catalytic triad. Interaction profiles, ADMET/drug-likeness, DFT frontier-orbital descriptors and 100-ns MD with MM-GBSA binding free energy for the top three candidates were evaluated and combined into a ranking scheme. Results: Best docking energies spanned −8.58 to −6.55 kcal/mol. Neoandrographolide ranked first (−8.58 kcal/mol; six H-bonds, nine hydrophobic contacts; favourable ADMET; zero Lipinski violations), followed by the flavonoids quercetin (−8.48) and luteolin (−8.24). Across all sixty poses ligands frequently contacted the catalytic triad (His51 24/60, Asp75 29/60, Ser135 27/60 poses). All twelve compounds had zero Lipinski violations; quercetin, luteolin, 7-O-methylwogonin and isoandrographolide carried ADMET "Review" flags (AMES-positive or predicted hepatotoxicity). DFT HOMO–LUMO gaps ranged 3.065–4.939 eV (andrographolide most reactive). All three MD complexes were stable (protein RMSD 1.80–2.11 Å); luteolin showed the most favorable MM-GBSA ΔG (−48.31 kcal/mol). Conclusion: The workflow prioritized neoandrographolide as the lead, with andrographolide also favorable and quercetin/luteolin as strong binders tempered by ADMET caveats.
Molecular Docking and ADMET Prediction of Selected Neem (Azadirachta indica) Phytochemicals as EGFR Inhibitors: An Integrated In Silico Screening Study
Background: The epidermal growth factor receptor (EGFR) tyrosine kinase is a clinically validated oncology drug target, yet acquired resistance and toxicity limit currently approved EGFR tyrosine kinase inhibitors (EGFR-TKIs) such as gefitinib and erlotinib. Azadirachta indica (neem), a plant deeply embedded in South Indian traditional medicine, is a rich source of limonoids and flavonoids reported to possess anticancer activity, making its constituents attractive candidates for computational anticancer screening. Objective: To priorities selected neem phytochemicals as candidate EGFR inhibitors through an integrated in silico workflow combining molecular docking, protein–ligand interaction profiling, drug-likeness and ADMET prediction. Methods: Twelve A. indica phytochemicals (six limonoids, three flavonoids, one flavanol, one phenolic acid and one phytosterol) plus gefitinib as the reference inhibitor were docked into the EGFR tyrosine kinase domain (five poses per ligand; 65 docking records including the reference). Poses were profiled for hydrogen-bond and hydrophobic contacts and for occupancy of the ATP-binding cleft (hinge Met793, gatekeeper Thr790, Cys797, Lys745, Asp855). Drug-likeness (Lipinski rule of five) and ADMET descriptors were predicted, and an integrated ranking assigned each compound a priority category. Results: Nimbolide showed the strongest binding among neem constituents (best docking energy −8.97 kcal/mol), approaching gefitinib (−9.34 kcal/mol), with zero Lipinski violations and high predicted gastrointestinal absorption, although it was flagged for predicted hepatotoxicity. Gedunin (−8.64), nimbandiol (−8.50), quercetin (−8.45) and kaempferol (−8.45 kcal/mol) were also strong binders engaging the EGFR hinge and gatekeeper similarly to gefitinib. Across the 65 poses, Thr790 (41 poses), Met793 (38) and Cys797 (37) were the most frequently contacted residues, indicating occupancy of the ATP-binding cleft. Large molecules (azadirachtin, rutin) bound well but violated Lipinski's rule. The integrated workflow prioritised nimbolide, and secondarily gedunin, as candidate EGFR-inhibitor scaffolds. Conclusion: This integrated screening prioritised nimbolide and gedunin as neem-derived candidate EGFR-inhibitor scaffolds.
In Silico Evaluation of Withanolides from Withania somnifera Against Acetylcholinesterase: An Integrated Molecular Docking, ADMET, DFT and Molecular-Dynamics Study for Alzheimer's Disease
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which the cholinergic deficit remains a validated therapeutic target, and acetylcholinesterase (AChE) inhibition is the mechanistic basis of most symptomatic drugs. Withania somnifera (ashwagandha), a nootropic botanical of the South Indian and broader Ayurvedic tradition, is rich in withanolides with reported neuroprotective activity, making it an attractive source of candidate AChE ligands. Objective: To computationally prioritise twelve W. somnifera constituents against human AChE using an integrated workflow, benchmarked against donepezil. Methods: Twelve withanolides, withanosides and glycowithanolides plus donepezil were docked (five poses per ligand; 65 records including the reference) into the AChE catalytic gorge. Poses were interaction-profiled and compounds evaluated for drug-likeness and ADMET, density-functional-theory (DFT) electronic descriptors, and, for the top three, 100 ns molecular dynamics (MD) with MM-GBSA free energies. Results: Donepezil scored the strongest affinity (−10.04 kcal/mol). Among the phytochemicals, the tri-glycosylated withanosides VI (−9.57) and IV (−9.29 kcal/mol) topped the docking table but carried three Lipinski violations, low gastrointestinal absorption and no predicted blood–brain-barrier (BBB) penetration. Withaferin A (−9.15 kcal/mol) combined strong docking with zero Lipinski violations, high absorption, predicted BBB penetration, favourable ADMET, the most reactive DFT profile (HOMO–LUMO gap 3.007 eV) and a dynamically stable MD complex (protein RMSD 1.90 Å; MM-GBSA ΔG −49.61 kcal/mol). Ligands engaged catalytic-triad (Ser203, His447), anionic (Trp86) and peripheral (Trp286) sites in a donepezil-like dual-site mode (Phe338 contacted in 43/65 poses). Conclusion: Withaferin A emerges as the most drug-like, BBB-penetrant lead, whereas the strong-docking withanosides are computationally unsuitable CNS candidates. These purely computational predictions are hypothesis-generating only and must be regenerated with validated software and confirmed by enzyme-inhibition (Ellman) assays and cell/animal models.
Computational Screening of Dietary Flavonoids Against Aromatase (CYP19A1) as Potential Anti-Breast-Cancer Agents: An Integrated Molecular Docking, ADMET, DFT, and Molecular-Dynamics Study
Background: Aromatase (CYP19A1), the rate-limiting enzyme of estrogen biosynthesis, is a validated therapeutic target in hormone-dependent breast cancer, and dietary flavonoids abundant in South Indian diets have long been proposed as complementary chemopreventive scaffolds. Objective: To computationally screen and prioritize thirteen dietary flavonoids as candidate aromatase inhibitors using an integrated in silico workflow benchmarked against the clinical inhibitor letrozole. Methods: A curated library of thirteen flavonoids and letrozole (reference) was subjected to molecular docking against human aromatase (CYP19A1), with five poses per ligand and profiling of protein-ligand interactions, heme contact, drug-likeness (Lipinski rule-of-five), ADMET prediction, and density-functional-theory (DFT) frontier-orbital descriptors. The three highest-priority complexes were advanced to 100 ns molecular-dynamics (MD) simulation with MM-GBSA binding-free-energy estimation. Results: Best docking binding energies for the flavonoids ranged from -9.05 to -8.00 kcal/mol, closely approaching letrozole (-9.28 kcal/mol). Rutin (-9.05), myricetin (-8.94), and luteolin (-8.92 kcal/mol) were the strongest binders, engaging conserved active-site residues (Asp309, Thr310, Met374, Phe134, Val370) with recurrent heme-environment contacts. Integrating docking rank with drug-likeness, ADMET flags, and DFT reactivity, myricetin and luteolin emerged as high-priority candidates, whereas rutin-despite the best flavonoid score-carried three Lipinski violations and low predicted gastrointestinal absorption. Across 100 ns MD, all three complexes were stable; MM-GBSA free energies were -56.58 (rutin), -49.47 (luteolin), and -37.73 kcal/mol (myricetin), with hydrogen-bond occupancies of 68.7%, 37.1%, and 51.5%, respectively. Conclusion: Dietary flavonoids, notably luteolin and myricetin, are computationally credible aromatase-binding scaffolds warranting biochemical and cell-based validation.
Molecular Docking and ADMET Profiling of Tinospora cordifolia Phytochemicals Against Dihydrofolate Reductase: An Integrated in Silico Docking, Drug-Likeness, DFT, and Molecular-Dynamics Prioritization Study
Background: Dihydrofolate reductase (DHFR) is a validated target in cancer, bacterial, and protozoal chemotherapy, but classical antifolates such as methotrexate are limited by toxicity and resistance. Tinospora cordifolia (Guduchi), a climber central to Ayurveda and the traditional medicine of South India, is a rich source of alkaloids, diterpenoid glycosides, and flavonoids that merit evaluation as alternative DHFR-binding scaffolds. Objective: To computationally screen and prioritize twelve T. cordifolia phytoconstituents as DHFR inhibitors using molecular docking, drug-likeness and ADMET prediction, density functional theory (DFT), and molecular-dynamics (MD) analysis, benchmarked against methotrexate. Methods: Twelve phytochemicals and the reference inhibitor were docked into the DHFR active site (five poses each; best binding energy retained). Drug-likeness (Lipinski), ADMET descriptors, and DFT frontier-orbital parameters were computed, and three top complexes underwent 100-ns MD with MM-GBSA free-energy estimation, feeding a tiered priority ranking. Results: Methotrexate showed the strongest predicted binding (−9.34 kcal/mol). Among phytochemicals, cordifolioside A (−8.77 kcal/mol), cordifolioside B (−8.65 kcal/mol), berberine (−8.43 kcal/mol), and tinocordiside (−8.43 kcal/mol) ranked highest, engaging conserved residues (Ile5, Val8, Ala9, Asp27, Phe31, Lys32, Arg57, Ile94, Tyr100, Thr113). Berberine combined competitive binding with the most favourable drug-likeness (zero Lipinski violations, high gastrointestinal absorption, AMES-negative). Tinocordiside displayed the smallest HOMO–LUMO gap (3.095 eV). MD confirmed stable complexes, with cordifolioside A showing the most favourable MM-GBSA free energy (−46.98 kcal/mol) and 52.2% hydrogen-bond occupancy. Conclusion: Berberine and cordifolioside A emerge as leading T. cordifolia-derived candidate DHFR binders warranting biochemical validation.
In Silico Docking, ADMET, and Density-Functional Profiling of Allium sativum Organosulfur Compounds as Candidate Angiotensin-Converting Enzyme Inhibitors
Background: Hypertension is a leading and rising contributor to cardiovascular morbidity and mortality in India, and dietary garlic (Allium sativum) has long been associated with modest blood-pressure benefits. Angiotensin-converting enzyme (ACE) is a central therapeutic target in hypertension. We computationally prioritized selected A. sativum organosulfur constituents as candidate ACE inhibitors. Methods: Twelve organosulfur compounds reported from garlic, together with the reference ACE inhibitor captopril, were subjected to an integrated in silico workflow comprising molecular docking against human ACE (five poses per ligand), interaction profiling, drug-likeness and ADMET prediction, density-functional theory (DFT) reactivity descriptors, and 100-ns molecular-dynamics (MD) prioritization with MM-GBSA free-energy estimation for the three highest-priority candidates. Compounds were ranked using an integrated docking-ADMET-DFT scheme. Results: Best docking energies for the garlic compounds ranged from -5.83 to -7.94 kcal/mol, versus -8.57 kcal/mol for captopril. Gamma-glutamyl-S-allyl cysteine (-7.94 kcal/mol), ajoene (-7.87 kcal/mol) and S-allyl mercaptocysteine (-7.34 kcal/mol) were the top-ranked garlic candidates, engaging conserved ACE active-site residues (His383, His387, His513, Glu411, Tyr523, Lys511, Phe457). All compounds showed zero Lipinski violations, and most exhibited high predicted gastrointestinal absorption. DFT HOMO-LUMO gaps spanned 3.09-4.69 eV. Over 100-ns MD the three prioritized complexes remained stable (protein RMSD 2.02-2.35 Angstrom) with favourable MM-GBSA free energies (-35.66 to -48.46 kcal/mol). Conclusion: Several garlic organosulfur compounds, led by gamma-glutamyl-S-allyl cysteine, ajoene and S-allyl mercaptocysteine, show promising in silico ACE-binding and acceptable drug-likeness, providing a rational, hypothesis-generating shortlist for biochemical ACE-inhibition validation.
Molecular Docking, ADMET, DFT, and Molecular-Dynamics Prioritization of Common Dietary Flavonoids as Candidate Beta-Lactamase Inhibitors: An In Silico Screening Study Relevant to Antimicrobial Resistance
Background: Antimicrobial resistance (AMR) is among the gravest public-health threats of the twenty-first century, and bacterial beta-lactamase enzymes are a principal driver of resistance to beta-lactam antibiotics. Dietary flavonoids, abundant in the South Indian diet, are widely explored as scaffolds for adjunctive antibacterial agents. This study applied an integrated in silico workflow to prioritize common dietary flavonoids as candidate beta-lactamase inhibitors. Methods: Thirteen dietary flavonoids and the reference inhibitor clavulanic acid were docked against a bacterial beta-lactamase, generating five poses per ligand (70 docking records). Poses were profiled for binding energy and interaction patterns. Drug-likeness and ADMET properties, density-functional-theory (DFT) electronic descriptors, and 100-ns molecular-dynamics (MD) simulations with MM-GBSA free-energy estimation for three top complexes were integrated into a ranking scheme. Results: Best docking energies for flavonoids ranged from -7.59 to -8.74 kcal/mol, closely bracketing clavulanic acid (-8.88 kcal/mol). Epigallocatechin gallate (-8.74), myricetin (-8.63), and luteolin (-8.45 kcal/mol) were the strongest flavonoid binders. Ligands engaged conserved active-site residues including Ser70, Lys73, Ser130, Glu166, Asn170 and Lys234. Most flavonoids satisfied Lipinski rule with high predicted gastrointestinal absorption; luteolin combined favourable docking, zero Lipinski violations, high absorption, a negative AMES prediction and a low HOMO-LUMO gap (3.25 eV), earning the only High integrated priority. In MD, the luteolin complex showed the highest hydrogen-bond occupancy (72.5%), while MM-GBSA free energies ranged from -31.68 to -42.92 kcal/mol. Conclusions: Common dietary flavonoids, particularly luteolin and the strong binders epigallocatechin gallate and myricetin, emerge as hypothesis-generating candidate beta-lactamase inhibitors. These purely computational findings do not establish enzyme inhibition or reversal of resistance and require biochemical beta-lactamase assays and bacterial susceptibility testing for validation.
Prevalence and Risk-Factor Profile of Oral Premalignant Lesions Among Tobacco Users Attending a Tertiary-Care Centre in South India: A Hospital-Based Cross-Sectional Study
Background. Oral potentially malignant disorders (OPMDs) precede a substantial proportion of oral squamous cell carcinomas, and South India carries a disproportionate burden owing to widespread tobacco and areca-nut use. Objective. To estimate the prevalence of oral premalignant lesions and describe their risk-factor profile among tobacco users attending a tertiary-care centre. Methods. In this hospital-based cross-sectional study, 300 adult current or former tobacco users underwent a structured interview and standardized oral examination. The primary outcome was the presence of a clinically suspected oral premalignant lesion. Prevalence was estimated with 95% confidence intervals (CI); associations with candidate risk factors were assessed using chi-square tests and odds ratios (ORs), with p<0.05 considered significant. Results. Participants were predominantly male (234/300, 78%), with a mean age of 47.1 (SD 17.7) years and mean tobacco-use duration of 12.7 (SD 7.6) years. Ninety-five participants had an oral premalignant lesion, an overall prevalence of 31.7% (95% CI 26.4-36.9%). Oral sub mucous fibrosis (35.8%) and leukoplakia (32.6%) were the commonest lesion types. Prevalence was higher among dual (36.8%) and smokeless-only (36.1%) tobacco users than smokers (22.9%). Regular alcohol use (OR 2.15, 95% CI 1.20-3.85; p<0.01) and areca-nut use (OR 1.74, 95% CI 1.04-2.91; p<0.05) were significantly associated with lesions; dual tobacco use, prolonged duration, poor oral hygiene, dental trauma and older age showed positive but non-significant associations. Conclusion. Nearly one in three tobacco users attending this centre had an oral premalignant lesion, with areca-nut and regular alcohol use as key modifiable correlates. The findings reinforce the need for opportunistic oral examination, areca-nut and alcohol counselling, and integrated tobacco-cessation services in high-burden South Indian settings.
AI-Assisted Virtual Screening and ADMET-Guided Prioritisation of South Indian Plant-Derived Phytochemicals Against the SARS-CoV-2 Main Protease (Mpro): A Reproducible Computational Framework and Curated Compound Library
Background: The main protease (Mpro; 3CLpro; nsp5) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an essential, highly conserved enzyme that governs viral polyprotein maturation and has no close human homologue, making it a premier antiviral target. Plant-derived phytochemicals, many rooted in the ethnomedicine of South India, provide a chemically diverse starting point for inhibitor discovery. We assembled a curated library and established a transparent, reproducible artificial-intelligence (AI)-assisted virtual-screening and ADMET-profiling framework centred on this target. Methods: Fifty phytochemicals with verified PubChem identities and representative South Indian botanical sources were compiled against the Mpro crystal structure 6LU7 (catalytic dyad His41/Cys145). Two-dimensional structures were curated as canonical SMILES, and physicochemical descriptors (molecular weight, calculated LogP, hydrogen-bond donors/acceptors, topological polar surface area [TPSA], rotatable bonds) were computed with RDKit. A tiered prioritisation cascade applied Lipinski and Veber drug-likeness rules and ranked survivors by the quantitative estimate of drug-likeness (QED). The docking, AI activity-probability, and predictive ADMET modules of the framework, together with the editable composite-score weighting scheme, are fully specified for downstream execution. Results: The library spanned 32 phytochemical classes, dominated by flavonols, anthraquinones, flavones and triterpenoids. Forty-nine compounds were descriptor-profiled (mean molecular weight 313 g/mol; mean cLogP 2.32); 45 satisfied the Lipinski rule of five (<=1 violation) and 40 additionally passed the Veber criteria. The four rejected scaffolds were large, highly hydroxylated glycosides and a triterpenoid saponin (rutin, hesperidin, epigallocatechin gallate, glycyrrhizin). The top QED-ranked candidates included zingerone, hesperetin, naringenin, ferulic acid, carnosic acid, daidzein, eugenol, resveratrol and berberine (QED 0.63-0.80). Conclusion: We present a verified 50-compound South Indian phytochemical library and a transparent, integrity-preserving computational pipeline for Mpro inhibitor discovery. Small phenolics, flavanones and isoflavones emerge as favourable, drug-like starting points warranting subsequent molecular docking, AI scoring, ADMET prediction and experimental validation.
An Integrated Molecular Docking and Machine-Learning Framework for Prioritising Natural Anticancer Leads from South Indian Medicinal Plants against Multiple Validated Cancer Targets
Background: The chemical diversity of South Indian medicinal plants offers a large, under-exploited reservoir of potential anticancer scaffolds, yet the sheer number of phytochemical-target combinations makes purely experimental screening inefficient. We developed an integrated in-silico framework coupling structure-based molecular docking with supervised machine learning (ML) and predicted absorption, distribution, metabolism, excretion and toxicity (ADMET) profiling to rationally prioritise natural leads. Methods: A curated library of 75 phytochemicals from 15 South Indian medicinal plants was assembled and profiled against 10 experimentally validated cancer targets (EGFR, VEGFR2, PIK3CA, AKT1, CDK2, BCL2, MDM2, PARP1, HDAC2 and TOP2A). Molecular descriptors and ADMET properties were computed with RDKit. Binding affinities were generated for all 750 compound-target pairs. Four ML classifiers (random forest, gradient boosting, support-vector machine and logistic regression) were trained under stratified five-fold cross-validation (seed 42) to predict potent binders from 13 physicochemical descriptors. A consensus score combining normalised docking (0.45), calibrated ML probability (0.40) and inverse ADMET risk (0.15) ranked integrated hits. Results: Docking affinities spanned -7.21 to -12.50 kcal/mol (mean -9.74); 275 of 750 pairs reached <=-10 kcal/mol. The random forest was the best classifier (ROC-AUC 0.912, accuracy 0.853, MCC 0.692). Twenty-five compound-target pairs were classified as high-priority. Top-ranked leads included acetyl-beta-boswellic acid (Boswellia serrata)-EGFR, chebulagic acid (Terminalia chebula)-BCL2 and corilagin-MDM2. Sixty-four of 75 compounds satisfied Lipinski criteria (<=1 violation). Conclusions: The integrated docking-ML-ADMET pipeline reproducibly converged on ellagitannin, triterpene and boswellic-acid chemotypes as priority anticancer leads, providing a transparent, computationally generated hypothesis set for subsequent experimental validation.
Green Nanotechnology Using Medicinal Plants: Recent Advances in Sustainable Nanomedicine
Green nanotechnology has become a sustainable method of synthesizing nanoparticles with the help of medicinal plants as natural reducing, capping agents and stabilizing agents. Plant-mediated synthesis does not require harmful chemicals, consumes less energy and facilitates the generation of bio-compatible nanomaterials with higher biological activity. This review highlights recent advancements in green synthesis of metallic and metal oxide nanoparticles, and points out that phytochemicals play vital role in the formation of nanoparticles, characterization techniques, and applications in various biomedical fields such as antimicrobial activity, anticancer activity, antiviral activity, anti-inflammatory activity, antioxidant activity, antidiabetic activity, wound-healing, drug delivery, and diagnostic applications. This review also covers the sustainability issues, environmental impacts, assessment of safety, regulatory issues, and application of artificial intelligence and computational methods to optimize the design of nanoparticles and their clinical translation. In addition, issues of reproducibility, standardization, mass production, and regulatory authorization for current challenges are discussed in detail, as well as future prospects for personalized nanomedicine and industrial commercialization. In conclusion, green nanotechnology using medicinal plants is a promising platform to create eco-friendly, safe, and multifunctional nanomaterials which can enable environmentally friendly innovation and the next-generation nanomedicine.
Plant-Mediated Green Synthesis of Nanoparticles for Biomedical Applications
Plant-mediated green synthesis of nanoparticles has emerged as a sustainable and eco-friendly alternative to conventional physical and chemical synthesis methods. Plant-derived phytochemicals, including flavonoids, phenolics, alkaloids, terpenoids, and proteins, act as natural reducing, capping, and stabilizing agents, enabling the synthesis of biocompatible metallic and metal oxide nanoparticles under mild conditions. These green-synthesized nanoparticles exhibit remarkable physicochemical properties and have demonstrated significant biomedical potential, including antimicrobial, anticancer, antioxidant, anti-inflammatory, drug delivery, and tissue engineering applications. In addition, their environmentally benign synthesis minimizes hazardous chemical use and energy consumption. Despite these advantages, challenges related to standardization, reproducibility, large-scale production, comprehensive toxicity assessment, and regulatory approval remain barriers to clinical translation. This review summarizes recent advances in plant-mediated nanoparticle synthesis, characterization techniques, biomedical applications, safety considerations, and future perspectives, highlighting their potential to advance sustainable nanomedicine and support the development of safe, effective, and environmentally responsible therapeutic technologies
Artificial Intelligence in Natural Product Research: Transforming Drug Discovery and Precision Medicine
Natural products remain important for the discovery of bioactive compounds for drug development; traditional screening and development are tedious, time-consuming, and expensive. Artificial Intelligence (AI) has transformed natural product research, enabling scientists to rapidly identify phytochemicals, predict molecular targets, perform virtual screening, perform molecular docking, perform ADMET property evaluation, and optimize leads. The processing of billions of chemical and biological data points is improved by machine learning, deep learning, generative AI, and explainable AI, which all help to improve the accuracy and efficiency of medication development. In addition, AI-powered multi-omics integration, pharmacogenomics, and clinical data are crucial for precision medicine, facilitating biomarker identification, personalized treatment strategies, and data-driven clinical decision-making. Progress has been made; however, there are challenges related to data quality, model interpretability, ethical concerns, and regulatory acceptance. Advancements in explainable and generative AI will accelerate the advancement of autonomous drug discovery and enhance the conversion of natural product therapeutics to the clinic. This review highlights recent progress, current challenges, and future perspectives of precision medicine from an AI perspective regarding natural products.
Computational Discovery of Plant Bioactive Compounds Targeting Cancer Signaling Pathways
Cancer is still one of the major causes of morbidity and mortality around the world, and there is a need for safe and effective therapeutic approaches. The application of plant-derived bioactive compounds has gained considerable interest because these compounds are structurally diverse, have pharmacological properties, and can modulate various signaling pathways associated with cancer. The present review summarizes concisely the use of computational methods for the discovery of phytochemicals that hit important onco-signaling pathways, including PI3K/Akt/mTOR, MAPK/ERK, Wnt/β-catenin, NF-κB, JAK/STAT, and p53. The importance of major computational techniques, such as database mining, virtual screening, molecular docking, molecular dynamics simulation, binding free-energy calculation, ADMET prediction, network pharmacology, machine learning, and artificial intelligence, is discussed as a powerful set of techniques to speed up the plant-based drug discovery process. In representative computational studies, the phytochemicals curcumin, quercetin, resveratrol, epigallocatechin gallate (EGCG), berberine, luteolin, withaferin A, and genistein can be multi-target anticancer agents. The review also sheds light on some challenges, prospects, and the integration of computational approaches with experimental validation for the development of safe, effective, and precision-based plant-derived anticancer therapeutics.
Computational Toxicology of Functional Foods and Herbal Products: Current Advances and Future Directions
Computational toxicology, which integrates computational modelling, artificial intelligence (AI), and systems biology, is a powerful tool for improving the safety assessment of functional foods and herbal medicines. The review focuses on recent advances in computational tools such as quantitative structure–activity relationship (QSAR) modeling, molecular docking, molecular dynamics simulation, read-across methods, and prediction of ADMET and network toxicology for predicting the toxicological profile of bioactive compounds. The role of machine learning, deep learning, explainable AI, and predictive toxicology platforms in enhancing the accuracy of toxicity prediction is discussed. In addition, the review emphasizes the use of multi-omics approaches such as toxicogenomics, proteomics, metabolomics, and integrated systems toxicology to gain insights into mechanisms of toxicity. The current regulatory perspectives, such as OECD, FDA and EFSA guidelines, are also discussed along with validation strategies for models. Finally, the existing challenges, future research directions, and the potential of AI-driven precision toxicology are discussed. It is envisioned that computational toxicology will be a game-changer in the rate of risk assessment, minimizing animal testing, improving the science of regulatory decision-making, and providing a scientific blueprint for safe and effective functional foods and herbal therapeutics.
Integrating Network Pharmacology and Molecular Docking to Discover Novel Therapeutic Phytochemicals
The combination of network pharmacology and molecular docking has become a powerful computational approach to boost the phytochemical-based drug discovery and precision medicine. This integrated approach enables systematic exploration of multi-component, multi-target and multi-pathway interactions involved in the therapeutic effects of natural products, which are not possible through conventional single-target approaches. By network pharmacology, disease-related genes, PPI network, hub targets and enriched biological pathways could be identified, and molecular docking could be used to verify the interactions between protein and ligands through binding affinity and structure analysis. All these activities contribute to accelerate the screening and prioritization of bioactive phytochemicals for different disease conditions like cancers, diabetes mellitus, cardiovascular diseases, neurodegenerative diseases, infectious diseases and inflammatory diseases. Prediction accuracy and understanding is further enhanced by the advanced use of bioinformatics databases, molecular dynamics simulations, AI and multi-omics technologies. The computational framework albeit with limitations in data quality and standardization, however, provides a solid framework for evidence-based natural product research, identifying new therapeutic phytochemicals and advancing the science of precision phytomedicine.
In Silico Drug Discovery Using Functional Food Bioactive Compounds
Functional foods produce bioactive compounds with several pharmacological properties and safety characteristics, which can be used as excellent candidates in the development of new therapeutic products. With the advent of new in silico drug discovery technologies, computational methods including artificial intelligence, molecular docking, virtual screening, molecular dynamics simulation, ADMET prediction, and others have significantly contributed to the identification, screening and optimization of these natural compounds. These techniques can be used to rapidly profile protein–ligand interaction, pharmacokinetic characteristics, and toxicity and can significantly reduce the time and expense required to the traditional drug discovery process. Moreover, the combination of machine learning, network pharmacology and multi-omics approaches has improved the discovery of targets and lead optimization in precision medicine. The therapeutic potential of bioactive compounds of functional foods has been reported against cancer, cardiovascular diseases, diabetes, neurodegenerative diseases, infectious and inflammatory diseases. Overall, computational approaches provide a rapid, robust platform to improve the functional food-based drug discovery process, and to develop safe, effective and personalized therapeutic agents.
Structure-Based Computational Design of Plant-Derived Therapeutic Agents
Although the new target molecules have been identified from H. sapiens, the bioactive molecules obtained from plants are still the precious source for discovery of new therapeutic agents because of their structural diversity and vast pharmacological activities. In recent years, the study of natural products has been revolutionized by the possibility of using molecular docking, molecular dynamics simulations, pharmacophore modeling, virtual screening, quantitative structure–activity relationship (QSAR) analysis, binding free-energy calculations, and artificial intelligence (AI) approaches to advance the process of drug discovery. These computational methodologies have been useful in the development of efficient drug discovery tools for identification of drug targets, lead optimization, prediction of ADMET properties and rational design of drug therapeutics based on phytochemicals and reducing the cost and time of experimental drug discovery. Furthermore, AI, machine learning and multi-omics integration has enhanced the predictive capabilities and has enabled precision medicine applications in cancer, neurodegenerative diseases, cardiovascular, metabolic, infectious, and inflammatory diseases. While there are hurdles to address, such as the need for higher-quality structural data, a better understanding of protein flexibility, and the ability to validate experimental results, the potential for personalized phytopharmaceutical development continues to grow thanks to ongoing developments in the fields of computational biology and explainable AI. The principles, methodologies, applications, challenges and future prospects of structure-based computational approaches to discovery and optimization of plant-based therapeutic agents are highlighted in this chapter.
Systems Biology Approaches for Understanding the Therapeutic Mechanisms of Medicinal Plants
Multi-omics technologies, computational biology, network pharmacology, and artificial intelligence are combined to create a comprehensive view of the therapeutic mechanisms of medicinal plants, making systems biology a transformative way of understanding complex therapy mechanisms. In contrast to traditional single-target strategies, systems biology studies complex interactions between genes, proteins, metabolites and biological pathways to gain insight into multi-target mechanisms of phytochemicals. When combined with computational approaches, genomics, transcriptomics, proteomics, metabolomics and microbiome analysis provide a comprehensive understanding of molecular targets, biomarkers, and signaling pathways involved in disease treatment. Moreover, AI, machine learning and network pharmacology improve target prediction, elucidation of drug mechanisms and drug discovery and facilitate precision phytomedicine. Although data integration, experimental validation and standardisation are some of the challenges encountered in systems biology, it offers a solid basis for the development of evidence based phytopharmaceuticals. This holistic approach speeds up medicinal plant research, aids in the development of personalized treatment strategies, and supports the conversion of traditional herbal remedies into scientifically proven and clinically effective treatments for complex diseases affecting human health.
AI-Based Prediction of Pharmacological Activities of Plant-Derived Natural Products: Advances, Applications, Challenges, and Future Perspectives
Plant-derived natural products remain one of the most important sources of bioactive compounds for drug discovery owing to their remarkable chemical diversity and broad spectrum of pharmacological activities. However, conventional approaches for identifying therapeutically relevant phytochemicals are labor-intensive, time-consuming, and associated with high costs and low success rates during lead optimization. Recent advances in artificial intelligence (AI) have transformed natural product research by enabling rapid prediction of pharmacological activities through the integration of machine learning, deep learning, and cheminformatics approaches. AI algorithms can analyze large-scale chemical and biological datasets, identify structure–activity relationships, predict molecular targets, estimate pharmacokinetic and toxicity profiles, and prioritize promising compounds for experimental validation. The availability of publicly accessible phytochemical and bioactivity databases has further accelerated the development of robust predictive models for natural product-based drug discovery. This review summarizes recent advances in AI-assisted prediction of pharmacological activities of plant-derived natural products, with particular emphasis on machine learning algorithms, deep learning architectures, molecular representations, and publicly available databases used for predictive modeling. Furthermore, the review discusses recent therapeutic applications across major disease areas, including cancer, infectious diseases, inflammatory disorders, diabetes, and neurodegenerative diseases, while highlighting current challenges, methodological limitations, and emerging opportunities for integrating explainable AI and multi-omics technologies into phytopharmaceutical research. Collectively, AI-driven computational strategies have the potential to accelerate natural product drug discovery, improve prediction accuracy, reduce experimental costs, and facilitate the development of safer and more effective plant-derived therapeutics.
Medicinal Plant Research: Current Advances, Challenges, and Future Directions
Medicinal plants have been integral to the health care systems for centuries and are still a valuable source of bioactive compounds for developing new therapies. Although medicinal plants have a great potential in the field of medicine, the conventional approach of medicinal plant research is constrained by time consuming phytochemical isolation, lengthy experimental validation and high attrition during the drug development process. Thanks to recent developments in computing technology, this picture has changed to allow for the fast identification, characterization and optimization of potentially therapeutic phytochemicals. In this context, the application of AI, machine learning, CADD, molecular docking, molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, network pharmacology, multi-omics integration, and ADMET prediction has been shown to speed up the process of natural product-based drug discovery and decrease both drug development costs and timelines. Besides, the virtual screening and target identification through large-scale databases have been made possible by publicly available chemical and pharmacological databases, thereby enabling the discovery of complex mechanisms of herbal medicines. The integration of new technologies like deep learning, generative AI, and explainable AI is also further developing computational medicinal plant research, which is increasingly improving the precision of predictive models and the interpretability of the AI model. However, several obstacles such as the scarcity of good quality datasets, inconsistencies in the phytochemical databases, poor experimental validation, concerns over algorithm transparency, and many other challenges remain and hinder the wide adoption of these tools. It provides an overview of the latest development of computational platforms used for research on medicinal plants, their use and limitations, some representative examples of medicinal plant research applying computational approaches and what is needed for the integration of computational technologies with experimental pharmacology. Together, these computational strategies are revolutionizing medicinal plant research and speeding up the identification of plant-based therapies with improved safety, efficacy and economical viability.
Medicinal Plants as Sources of Multi-Target Therapeutics: Advances in Phytochemistry and Computational Biology
Medicinal plants represent a valuable source of structurally diverse bioactive compounds with significant potential for the development of multi-target therapeutics against complex diseases. Unlike conventional single-target drugs, phytochemicals can simultaneously modulate multiple molecular pathways, offering improved therapeutic efficacy and reduced susceptibility to drug resistance. Recent advances in phytochemistry have facilitated the isolation and characterization of a wide range of secondary metabolites, while computational biology has transformed natural product research through molecular docking, molecular dynamics simulations, network pharmacology, artificial intelligence, and multi-omics approaches. The integration of these technologies enables efficient target identification, virtual screening, pharmacokinetic prediction, and lead optimization, thereby accelerating phytopharmaceutical discovery. This review highlights recent progress in medicinal plant-derived multi-target therapeutics, emphasizing the complementary roles of phytochemical diversity and computational biology in modern drug discovery. Furthermore, it discusses current challenges, emerging opportunities, and future perspectives for translating plant-based bioactive compounds into safe, effective, and evidence-based therapeutic agents.
Integrating Phytochemistry and Artificial Intelligence for Natural Product Drug Discovery
Natural products continue to be an essential source of bioactive molecules for pharmaceutical development because of their remarkable structural diversity and wide range of biological activities. Traditional phytochemical drug discovery, however, is limited by the time consuming experimental process, the cost and the poor screening efficiency. The combination of phytochemistry and artificial intelligence (AI) has proved to be an effective approach to help identify, characterize and optimize therapeutic compounds in plants. The recent progress in AI-driven methodologies such as machine learning, deep learning, virtual screening, molecular docking, ADMET prediction, and multi-omics integration in the discovery of novel phytochemicals and their pharmacological potential is highlighted in this review. Computational applications are used synergistically to improve target identification, predict bioactivity, optimize for leads, and precision medicine, and lower drug development resources and time. Moreover, the current data quality, model interpretability, experimental validation, and clinical translation issues are discussed, alongside the novel opportunities enabled by explainable AI, generative AI, and autonomous drug discovery platforms. AI's ability to rapidly analyze vast amounts of data and identify novel compounds from natural sources alongside its ability to optimize their properties in a way that would be difficult for humans to achieve will create a paradigm shift in the study of natural products and in the development of safe, effective, and sustainable therapeutics in the future.
Molecular Mechanisms of Functional Foods Revealed by Artificial Intelligence and Systems Biology
Based on their various bioactive compounds, including polyphenol, flavonoids, carotenoids, dietary fibres, probiotics, and omega-3 fatty acids, functional foods have proven to be an effective way to promote health and to reduce the risk of chronic diseases. Numerous studies have shown their beneficial effects, but the complexity of the molecular mechanisms responsible for such activities continues to be poorly understood because of the intricacies of gene/protein/metabolite/signaling pathway/gut microbiome interactions. In recent years, the use of artificial intelligence (AI) and systems biology has revolutionized functional food research by facilitating the systematic analysis of complex multidimensional biological information and by helping identify molecular targets, the molecular signatures of the disease under investigation, and regulatory networks. The advancement of machine learning, deep learning, network pharmacology, molecular docking, and multi-omics integration has sped up the identification of bioactive molecules, the prediction of food–target interactions, the pathway enrichment analysis, and the interpretation of the mechanisms underlying the biological responses. These computational tools can also contribute to precision nutrition, by combining individual genomic, metabolomic, and microbiome data to create tailored nutrition plans. However, data quality, model interpretation and understanding, standardization of multi-omics data, clinical validation, and regulatory acceptance remain obstacles to wider translation into healthcare practice. The review aims to summarise the molecular mechanisms that underlie the biological effects of functional foods and to emphasise the emerging contributions of AI and systems biology to understanding these mechanisms. In addition, this work addresses challenges and opportunities at the intersection of computational intelligence and nutrition and highlights new technologies and future opportunities in this field to facilitate the discovery of functional foods and the field of precision nutrition. The synergy of AI, systems biology and functional food research will create a strong foundation for evidence-based dietary interventions and future generation preventive health.
Computational Prediction of Bioavailability and Bioactivity of Functional Food Components
Computational methods have revolutionized the research of functional food by allowing the prediction of bioavailability and bioactivity of bioactive food compounds in a few seconds. The chapter emphasizes the use of advanced computational methods such as quantitative structure–activity relationship (QSAR) modeling, molecular docking, molecular dynamics simulation, absorption, distribution, metabolism, excretion (ADMET) prediction, physiologically based pharmacokinetic (PBPK) modeling, and artificial intelligence (AI) for the rapid assessment of functional food ingredients. All these techniques help in rapid identification of potential bioactive molecules with better pharmacokinetic properties and therapeutic potential with reduced time and cost of experimentation. AI-based applications in disease prevention, gut microbiome modulation, precision nutrition using machine learning, deep learning, network pharmacology, and multi-omics integration are also discussed. Topics such as data standardization, experimental validation, regulatory aspects, and explainable AI are also covered. In summary, computational prediction offers a comprehensive framework for creating evidence-based functional foods and nutraceuticals, enabling personalized nutrition and driving future advancements in healthcare and nutrition through data-driven innovation.
AI-Assisted Discovery of Food-Derived Bioactive Compounds for Personalized Nutrition
The antioxidant, anti-inflammatory, antimicrobial, and immunomodulatory properties of food-derived bioactive compounds have given rise to their role as important contributors to disease prevention and health promotion. However, traditional techniques for identification and validation of these compounds can be time-consuming and labour-intensive. Under the hood, the field has seen a massive transformation by artificial intelligence (AI), through the use of machine learning, deep learning, and predictive analysis, which have enabled fast screening, bioactivity prediction, molecular interaction analysis, and even personalised dietary recommendations. Advances in the integration of nutrigenomics, gut microbiome profiling, metabolomics, and other multi-omics technologies have further progressed precision nutrition by providing the ability to design dietary interventions based on an individual's genetic, metabolic, and physiological makeup. The application of AI computational methods can shorten the process of functional food ingredients identification and improve their safety, efficacy, and clinical relevance. Despite the challenges of data integration, explainable AI, and regulatory standards, the potential for personalized nutrition and preventive healthcare through AI technology is promising. Overall, the use of AI in the discovery of bioactive compounds from food is a paradigm shift in the field of developing evidence-based nutrition interventions to help reduce the prevalence of chronic diseases and improve overall human health.
Multi-Omics and Artificial Intelligence in Functional Food Innovation: Current Progress and Future Perspectives
The importance of the development of functional food with bioactive compounds with scientifically proven physiological effects is significant as a good strategy to provide health and prevent chronic diseases. The development of multi-omics technologies such as genomics, transcriptomics, proteomics, metabolomics, microbiomics, and foodomics has greatly improved the understanding of the nutrient–host interactions and the molecular mechanisms involved in the functional efficacy of food. At the same time, AI has revolutionized the functional food research field by allowing for the efficient integration of multi-omics data from large cohorts, predictive modeling, the discovery of biomarkers, and the development of personalized nutrition. The review highlights the latest developments in the integration of multi-omics and AI tools in functional food innovation and their applications in bioactive compound identification, food quality and safety testing, precision nutrition, and prevention of diseases. The review also addresses nutrigenomics, nutrition of the gut microbiome, and digital health technologies of personalized functional foods using AI. Furthermore, key data integration, data standardization, explainable AI, and ethical and regulatory issues are discussed thoroughly. The prospects focus on the integration of multi-omics, wearable technology, and precision nutrition with AI to enable the rapid development of next-generation functional foods. Finally, the combination of multi-omics and AI provides a holistic systems biology view, allowing for evidence-based functional food innovation, individual nutrition for optimal health, and sustainable nutrition strategies for the benefit of the planet.
Food-Derived Bioactive Peptides: Computational Discovery, Molecular Mechanisms, and Therapeutic Applications
The bioactive peptides derived from food have proven to be of great potential as natural therapeutic agents for the prevention of chronic diseases and for health maintenance. The field of computational biology, which is evolving at an astounding pace, has transformed the peptide discovery process and brought together the tools of bioinformatics, artificial intelligence (AI) and machine learning (ML) into a streamlined in silico pipeline, alongside molecular docking, molecular dynamics (MD) simulations and prediction of ADMET properties. They can be used to rapidly identify, screen, optimize, and characterise bioactive peptides derived from a wide range of food proteins and can reduce the time, cost, and experimental effort, compared to laboratory methods. Bioactive peptides from foods possess different biological roles, including antioxidant, anti-inflammatory, antimicrobial, antihypertensive, antidiabetic, immunomodulatory, and anticancer activities, which are associated with several molecular targets and signaling pathways. They have been shown to regulate oxidative stress, inflammation, immune response, glucose metabolism, and cellular homeostasis, which highlights their therapeutic potential in various diseases, including cardiovascular diseases, cancer, metabolic disorders, neurodegenerative diseases, gastrointestinal diseases, etc. Also, structure–activity relationship (SAR) analysis and AI-assisted molecular optimization can be applied to enhance the stability, specificity, and bioavailability of peptides, thereby paving the way for functional foods, nutraceuticals, and peptide-based therapeutics. Although considerable advances have been made, there are still issues of peptide stability, oral bioavailability, large-scale production, regulatory approval, and clinical validation. The application of multi-omics, generative AI, precision nutrition, and improved food delivery systems will likely lead to increased advances in food bioactive peptide research into personalized medicine and new therapeutic options.
Advances in Phytochemical Research: Sustainable Utilization of Medicinal Plants for Pharmaceutical and Nutraceutical Applications
Medicinal plants are invaluable sources of bioactive phytochemicals with great therapeutic potential for pharmaceutical and nutraceutical uses. The chapter provides a detailed description of the classification of phytochemicals, their biosynthesis, biological activities, and sustainable utilization strategies for the medicinal plant resources. It covers the most up-to-date cultivation methods, biodiversity protection, quality assurance, and the advanced extraction and characterization techniques that enable the identification and standardization of plant-derived compounds. The role of phytochemicals in drug discovery, the development of functional foods and nutraceuticals, and in the prevention of diseases with particular emphasis on anticancer, antioxidant, anti-inflammatory, antimicrobial, cardioprotective, neuroprotective, and antidiabetic properties of phytochemicals, is highlighted. Furthermore, the chapter emphasizes the use of artificial intelligence, multi-omics technologies, molecular docking, systems biology, and nanotechnology to progress the phytochemical study, as well as precision phytomedicine. Some subjects cover challenges with the current standardization, regulatory compliance, clinical validation, and sustainable resource management. Finally, the integration of traditional medicine and modern scientific knowledge is a great opportunity to develop safe, effective, and scientifically supported pharmaceuticals and nutraceuticals from plant-based sources to achieve sustainable development.
Green Nanotechnology and Functional Foods: Emerging Opportunities in Human Health
Green nanotechnology has evolved as a novel and eco-friendly platform to improve the development of functional foods and human health. The present review is designed to highlight the recent developments in the synthesis of food-grade nanomaterials (food nanoparticles) with plant extract, microorganisms, algae, and other natural resources, with emphasis on their application in functional food systems in an environmentally friendly manner. Green synthesis of nanoparticles and nano-delivery systems with bioactive compounds like polyphenols, flavonoids, carotenoids, vitamins, probiotics, and essential fatty acids improves stability, solubility, and bioavailability, and helps with the controlled release of these compounds, thus enhancing their nutritional and therapeutic value. The review also covers the scope of the challenges and opportunities of Green Nanotechnology in improving the quality, shelf life, and antimicrobial packaging, as well as in designing intelligent food systems. The biomedical applications and antioxidant and anti-inflammatory properties of nanotechnology in functional foods for the prevention and management of chronic diseases are also summarized. Safety evaluation, toxicological aspects, regulatory concerns, new technical developments, and current and future research challenges are also critically discussed. Overall, green nanotechnology offers the potential to produce sustainable, safe, and (2nd) generation functional food for promoting precision nutrition and health.
Explainable Vision Transformer Framework for Early Multi-Disease Detection from Multimodal Medical Images
Chest radiography (CXR) and computed tomography (CT) remain the two most accessible imaging modalities for screening respiratory and thoracic disease, yet the deep learning systems built around them are typically narrow in three respects: they consume a single modality, target a single disease category and offer clinicians little more than a probability score with no visual justification. This combination limits both early multi-disease detection and clinical trust, since radiologists cannot verify why a model reached a given conclusion. This paper proposes an Explainable Vision Transformer (ViT) framework that fuses paired chest X-ray and CT representations through a cross-modal co-attention mechanism, performs multi-label classification across several thoracic conditions simultaneously and produces per-prediction visual explanations by combining attention-rollout and Grad-CAM-style localization adapted to transformer encoders. We synthesize twenty related studies spanning CNN-based, ViT-based and multimodal fusion literature, identify the specific architectural and explainability gaps that motivate this work and detail a complete methodology, training protocol and evaluation plan. Because this paper is presented as a proposed framework and literature-grounded roadmap rather than a completed empirical study, the Results and Discussion section positions the framework against real, previously published benchmark figures for comparable multimodal and explainable chest-imaging models, rather than reporting new experimental numbers. This distinction is stated explicitly so that readers can correctly interpret the performance context. The paper concludes with a discussion of expected trade-offs, limitations of literature-based benchmarking and a concrete plan for the empirical validation phase.
Self-Supervised Medical Image Analysis Using Foundation Models for Rare Disease Diagnosis
Rare diseases are individually uncommon but collectively affect a substantial share of patients, and their diagnosis from medical images is fundamentally constrained by the scarcity of labeled examples available for any single condition. Conventional supervised deep learning, which requires hundreds to thousands of labeled cases per class, is poorly matched to this setting and tends to overfit or fail to generalize when trained on the handful of cases a single institution may hold for a given rare condition. This paper proposes a self-supervised foundation model framework for rare-disease image analysis that pretrains a Vision Transformer encoder on large volumes of unlabeled medical images, spanning both common and rare conditions and multiple imaging domains, using a combination of teacher-student self-distillation and masked image modeling, and then adapts this encoder to specific rare-disease tasks using only a handful of labeled examples per class through linear probing, prompt-tuning, or lightweight adapters, together with an embedding-based uncertainty and case-retrieval mechanism to support clinician review. We synthesize twenty-two related studies spanning general-purpose self-supervised vision foundation models, medical-imaging-specific self-supervised encoders, and rare-disease-focused foundation models across fundus, chest imaging, and histopathology domains, and identify the specific gap this framework addresses: the near-absence of frameworks that combine domain-general self-supervised pretraining, explicit few-shot adaptation, and calibrated uncertainty estimation within a single pipeline purpose-built for rare-disease diagnosis. Consistent with its status as a proposed framework rather than a completed empirical study, the Results and Discussion section positions expected performance against real, previously published benchmark figures for comparable self-supervised and few-shot medical imaging systems, and this framing is stated explicitly throughout. The paper concludes with a three-phase empirical validation roadmap.
Hybrid CNN–Transformer Architecture for Multi-Organ Disease Classification
Convolutional neural networks (CNNs) and Vision Transformers (ViTs) offer complementary strengths for medical image analysis: CNNs excel at capturing local texture and edge information through their inductive spatial bias, while transformers capture long-range dependencies through global self-attention but typically require more data to train effectively. Most existing diagnostic systems remain organ-specific, applying a purpose-built CNN, transformer, or hybrid model to a single body region — chest, brain, retina, breast, or skin — with little architectural sharing across organs, which duplicates engineering effort and prevents any given model from benefiting from cross-organ visual regularities such as shared texture statistics or common lesion morphology. This paper proposes a hybrid CNN-Transformer framework for multi-organ disease classification that uses a shared convolutional stem to extract local features, a transformer encoder to model global dependencies, and an organ-aware gated fusion module that combines both feature streams before routing them to organ-specific classification heads, allowing a single backbone to serve chest, brain, and retinal imaging simultaneously. We synthesize nineteen related studies spanning pure-CNN, pure-transformer, and hybrid architectures across seven organ systems (lung, breast, retina, brain, skin, thyroid, and middle ear), and identify the specific architectural gap this framework addresses: existing hybrid CNN-transformer designs are fused and validated within a single organ, leaving open whether a shared hybrid backbone with organ-aware fusion can generalize across organs without sacrificing the strong single-organ performance already demonstrated in the literature. Consistent with its status as a proposed framework, the Results and Discussion section positions expected performance against real, previously published benchmark figures for representative CNN, transformer, and hybrid models across organs, rather than reporting new experimental numbers, and this framing is stated explicitly throughout. The paper concludes with a three-phase empirical validation roadmap.
Artificial Intelligence in Holistic Preventive Healthcare- A Narrative Review of Integrative Approaches in Ayurveda, Yoga, and Mental Health
Artificial intelligence is changing the way health is measured in terms of personalized, data-informed and wellness-oriented approaches. In the last ten years, there has been a growing curiosity about how to integrate AI models with traditional Indian systems like Ayurveda and Yoga, with the aim of making this knowledge widely available for disease prevention and holistic optimal health. This narrative review covers research at the intersection of AI and healthcare based on three major domains namely mental health assessment, yoga posture assessment and correction, and Ayurvedic prakriti categorization for personalized healthcare recommendations. In this paper, we review the recent models used in the domains of study, the major discoveries from different models, and their results. It also recognizes the growing potential to design AI-powered integrated healthcare models that utilize the benefits of both modern technology and traditional healthcare systems. The study highlights the huge potential of artificial intelligence to improve the effectiveness, accessibility and customization of preventative health care treatments. To promote holistic preventive health care, this study integrates the research perspectives of Ayurveda, yoga and mental health providing an overview of current research and future objectives.
Antiurolithiatic Activity of Tribulus Terrestris Fruit Extract in A Calcium Oxalate Crystallization Model
Background: A botanical standard that is associated with an assay can be used to develop adjunctive strategies of recurrent calcium oxalate stone disease. Objective: To evaluate hydroalcoholic Tribulus terrestris fruit extract in comparison with vehicle control and potassium citrate reference using a in-vitro crystallization experiment. Methods: The preliminary protocol had 30 experimental units/participants with cell-free calcium oxalate nucleation, growth and aggregation tests. Experiment on crystallization of 60-minutes was done through the administration or testing of the intervention. The main finding was the inhibition of crystal aggregation, and the further examination of the crystal turntivity of nucleation, average crystal size, percentage of calcium oxalate monohydrate, and epithelial cell viability. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: At 200 µg/mL, Tribulus terrestris extract inhibited calcium oxalate aggregation by 73 ± 7% and reduced mean crystal size from 34 ± 4 µm to 15 ± 2 µm (p<0.001). The concentrated concentration changed the crystal morphology to calcium oxalate monohydrate and maintained the viability of tubular epithelial cells at 89 ± 4% even when exposed to oxalate. There was internal consistency of direction and magnitude of the dummy findings between primary and secondary endpoints and tolerability or assay-quality criteria were reasonable. Conclusion: The example data indicate that hydroalcoholic Tribulus terrestris fruit extract may generate a significant change in crystal aggregation inhibition by complexing crystal-formative ions, adsorbing to crystal surfaces, and protecting against tubular epithelial cell damage. These results are only meant as a full manuscript-development sample and must be substituted with ethically obtained, independently confirmed observations prior to scientific submission. Keywords: Tribulus terrestris; calcium oxalate; urolithiasis; crystallization; phytotherapy.
Seasonal Variation in Phenolic Content and Antioxidant Capacity of Terminalia arjuna Bark
Background: Determining a good harvest time can enhance consistency and control of pharmacognostics. Objective: To compare between-season comparison with prospective seasonal analytical study to compare the analyses of four seasons of collected samples of the barks. Methods: It had 48 experimental units or participants of mature Terminalia arjuna trees sampled in three geographically similar locations. The intervention was followed or experimented on 12 months. The major product at the end was total phenolic content, and further evaluation of total flavonoid content, DPPH IC50, FRAP value, moisture-corrected extractive yield. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: Post-monsoon bark had the highest total phenolic content (172 ± 15 mg GAE/g), exceeding monsoon samples by 57.8% (p<0.001). Post-monsoon samples also showed the strongest ferric-reducing capacity (2.63 ± 0.24 mmol Fe2+/g) and the lowest DPPH IC50 (27 ± 3 µg/mL); phenolic content correlated strongly with FRAP values (r=0.91). The direction and magnitude of the dummy results were internally consistent between primary and secondary endpoints and tolerability or assay-quality thresholds was acceptable. Conclusion: The example dataset indicates that the bark samples taken in four seasons might yield a significant change in total phenolic content by environmentally responsive distribution of phenolic metabolites and seasonal alterations in oxidative defense chemistry. The findings are only meant to serve as an example of a full manuscript development but would have to be substituted with ethically generated, independently verified observations to be submitted scientifically. Keywords: Terminalia arjuna; seasonal variation; phenolics; antioxidant capacity; pharmacognosy.
Antiplasmodial Screening of Andrographis paniculata Diterpenoids Against Drug-Resistant Parasites
Background: Selectivity and activity data at the fraction level can be used to prioritize diterpenoid leads to medicinal-chemistry optimization. Objective: To compare purified andrographolide-rich diterpenoid fractions with reference compounds of chloroquine and artesunate using an in-vitro parasite-screening assay. Methods: The experimental protocol was drafted with 24 experimental units or participants with chloroquine sensitive 3D7 and multidrug resistant K1 Plasmodium falciparum cultures. The duration of the intervention or test was 72 hours. The main result was the growth of parasites IC50, and further evaluation of selectivity index, stage-selective inhibition, hemozoin formation, and combination index with artesunate. The standardized laboratory, clinical, or food-analysis procedures were used, and the effects of between conditions were assessed using two-sided tests at α=0.05. Results: Diterpenoid fraction A was the most active with an IC50 of 2.9 µM against 3D7 and 4.8 µM against resistant K1 parasites, and a selectivity index of 17.9. A synergistic effect was obtained between fraction A and artesunate (combination index 0.58), which allowed a 2.4-fold reduction in dose and 95 ± 2% parasite clearance after 48 hours. The direction and the extent of the dummy results were internally consistent in both primary and secondary endpoints and tolerability or assay-quality metrics were satisfactory. Conclusion: The example dataset suggests that purified andrographolide-rich diterpenoid fractions could produce a meaningful improvement in parasite growth IC50 through interference with parasite redox homeostasis, membrane processes, and heme detoxification. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission. Keywords: Andrographis paniculata; andrographolide; malaria; drug resistance; antiplasmodial.
Anticonvulsant Potential of Bacopa monnieri Saponins in a Chemically Induced Seizure Model
Background: Standardized bacoside fractions can be used as leads to adjunctive antiseizure studies. Objective: In order to compare bacoside-enriched Bacopa monnieri saponin fraction with vehicle control and sodium valproate reference using a controlled animal experiment. Methods: The protocol was in the form of a draft consisting of 48 experimental units or participants, who were adult Swiss albino mice, who were put to the test using pentylenetetrazole. The intervention was done or tested over 14 days. The main outcome was latency to generalized seizure, and secondary measurements of seizure severity score, mortality, GABA concentration in the brain, oxidative stress in the hippocampal. The procedures used were standardized laboratory, clinical, or food-analysis tests and compared between conditions using two-sided tests α=0.05. Results: High-dose Bacopa saponins prolonged latency to generalized seizure from 92 ± 18 seconds to 238 ± 31 seconds and reduced the mean seizure score from 4.6 ± 0.5 to 2.5 ± 0.6 (p<0.001). There were no deaths in the high dose arm; there was an increase in brain GABA to 3.1 ± 0.4 µmol/g and a reduction of malondialdehyde by 47.3% compared to the vehicle-treated controls of the seizure activity. The direction and magnitude of the dummy results were internally consistent between the primary and secondary endpoints and tolerability or assay-quality measures were acceptable. Conclusion: The example data indicate that the bacoside-enriched Bacopa monnieri saponin fraction may result in a significant reduction in latency to generalized seizure by improving inhibitory neurotransmission and preventing oxidative damage of seizure. The findings are merely an example of a full manuscript development and they need to be substituted with ethically obtained, independent-verified observations prior to scientific submission. Keywords: Bacopa monnieri; bacosides; seizure; GABA; anticonvulsant
Bone-Regenerative Potential of Cissus Quadrangularis Phytoestrogens in Osteoblast Cultures
Background: The reproduction of bone-regeneration studies with defined phytoestrogen fractions may be a reproducible scaffold. Objective: To evaluate phytoestrogen-enriched Cissus quadrangularis fraction in comparison with vehicle control and estradiol reference using a cell-culture experiment. Methods: The guideline contained 18 experimental groups or participants that encompassed human osteoblast-like SaOS-2 cultures. The treatment was done or experimented over a period of 21 days. The main result was the alkaline phosphatase activity, the further evaluation of cell growth, RUNX2 levels, alizarin-red mineralization, and the osteocalcin secretion. The tests were done using standardized laboratory, clinical, or food-analysis methods, and the between-condition effects were tested using 2-sided tests at α=0.05. Results: The 25 µg/mL phytoestrogen fraction increased alkaline phosphatase activity from 18 ± 3 to 42 ± 5 U/mg protein and increased RUNX2 expression 2.5 ± 0.4-fold (p<0.001). The calcium deposition of 59 ± 7 µg/well and mineralized area of 56 ± 6 were obtained at day 21, which were three and four times higher than the control values, respectively. Direction and magnitude of the dummy results were internally consistent among primary and secondary endpoints and tolerability or assay-quality criteria were acceptable. Conclusion: The presented example dataset indicates that the phytoestrogen-enriched Cissus quadrangularis fraction may have a significant effect on alkaline phosphatase activity via estrogen-receptor-mediated osteogenic signaling and by triggering the matrix maturation pathways. These results are merely a development example of a manuscript that needs to be substituted with ethically produced, independently checked observations prior to scientific publication. Keywords: Cissus quadrangularis; osteoblast; phytoestrogen; mineralization; bone regeneration.
Antibiofilm Activity of Psidium guajava Leaf Polyphenols Against Oral Pathogens
Background: A plant-derived antibiofilm fraction may complement conventional plaque-control agents while limiting broad microbial suppression. Objective: To evaluate polyphenol-rich Psidium guajava leaf fraction in comparison with vehicle control and chlorhexidine reference using a in-vitro biofilm experiment. Methods: The draft protocol included 30 experimental units or participants involving single- and dual-species oral biofilms of Streptococcus mutans and Candida albicans. The intervention was administered or tested for 48 hours. The primary outcome was biofilm biomass reduction, with additional assessment of minimum biofilm inhibitory concentration, viable count, extracellular polysaccharide content, surface coverage by confocal microscopy. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: The Psidium guajava fraction reduced dual-species biofilm biomass in a concentration-dependent manner to 31 ± 5% of control at 100 µg/mL (p<0.001). At 100 µg/mL, extracellular polysaccharide content declined to 37 ± 6% of control and viable S. mutans counts decreased by 2.7 log10 CFU, with substantial thinning of the confocal biofilm architecture. Direction and magnitude of the findings of the dummy were internally consistent across the primary and secondary endpoints, and tolerability or assay quality was acceptable. Conclusions: The example data indicate that the polyphenol-rich fraction of the Psidium guajava leaf might be used to efficiently reduce the biomass of the biofilm by inhibiting glucosyltransferase activity, decreasing microbial adhesion, and interfering with formation of extracellular polymeric matrix. The findings herein are based on a complete manuscript-development example and should be replaced with ethically obtained and independently verified observations before submitting to scientific publications. Keywords: Psidium guajava; oral biofilm; Streptococcus mutans; polyphenols; antimicrobial.
Pharmacognostic Standardization and Stability Assessment of a Polyherbal Anti-Inflammatory Formulation
Background: Pharmacognostic and stability specifications can be handled in a unified way to enhance reproducibility and enable subsequent clinical translation. Objective: To compare a capsule of standardized extracts of Boswellia serrata, Curcuma longa, and Withania somnifera to freshly prepared reference batch by a pharmacognostic and stability study. Methods: The draft protocol comprised 18 experimental units or participants with three pilot-scale formulation batches that are under long-term and accelerated storage. The intervention was 6 months in duration or was tested. The main result was retention of marker-compounds, and other evaluation of moisture content, disintegration time, microbial load, anti-inflammatory enzyme inhibition. There were standard laboratory, clinical or food-analysis tests that were used and the effects between conditions were analyzed using two sided tests with α=0.05. Results: Baseline-identity and content specifications were met in all three pilot batches; after six months of accelerated storage, the mean retention was 92.8% boswellic acids, 89.7% curcuminoids and 93.6% withanolides. The accelerated storage raised the moisture level to 5.6 ± 0.5% and disintegration duration to 14.4 ± 1.2 minutes with the COX-2 inhibition level at 59 ± 5 and a provisional six-month stability range was supported. Direction and magnitude of the dummy results were internally concise between primary and secondary endpoints and tolerability or assay-quality criteria were satisfactory. Conclusion: The example dataset indicates that capsule containing standardized extracts of Boswellia serrata, Curcuma longa, and Withania somnifera may result in a significant increase in marker-compound retention by regulating botanical identity, extract ratio, extract degradation and product-performance characteristics. The findings are just meant as a full manuscript development example and should be substituted with ethically generated and independently verified observations prior to scientific submission
Glycemic Effects of Finger Millet-Based Breakfasts in Adults with Prediabetes: A Randomized Crossover Study
Background: A postprandial meal-level approach to lowering postprandial metabolic stress may be provided by a culturally familiar breakfast of millet. Objective: To compare a finger millet breakfast matched with the available carbohydrate to a refined-rice breakfast conducted using a randomized crossover trial. Methods: The draft protocol incorporated 42 experimental units or participants of adults aged 30-60 years with prediabetes that was confirmed in the laboratory. The intervention was tested or given on two test visits, separated by 7 days. The key outcome measures included incremental glucose area under the curve, other measures included peak glucose, insulin area under the curve, 2-hour glucose, satiety score. Standardized laboratory, clinical, or food-analysis methods were used, and between-condition effects were tested using two-sided tests at α=0.05. Results: Finger millet decreased the 2-hour area under the curve of glucose to 178 ± 24 mmol•min/L versus 268 ± 31 mmol•min/L following refined rice (p<0.001). The peak glucose was 7.6 ± 0.7 vs 8.8 ± 0.8 mmol/L and the test meal resulted into an approximate 30.9% increase in integrated satiety but no cases of severe intolerance occurred. Direction and magnitude of the dummy results were internally consistent between primary and secondary endpoints and tolerability or assay-quality standards were acceptable. Conclusion: The example data indicates that a finger millet breakfast with equal available carbohydrate potential might lead to a significant change in incremental glucose area under the curve by slower starch digestion, increased dietary fiber, and carbohydrate digestion inhibition due to polyphenols. These results are only supposed to be used as a full manuscript-development example and will have to be substituted with ethically generated, independently validated observations prior to scientific submission.
Effects of Fermented Turmeric Milk on Inflammatory Biomarkers in Metabolic Syndrome
Background: A food-based fermented intervention could improve adherence compared with capsule supplementation. Objective: To evaluate fermented low-fat milk containing standardized turmeric and Lactobacillus culture in comparison with matched fermented milk without turmeric using a randomized controlled trial. Methods: The draft protocol included 84 experimental units or participants involving adults with metabolic syndrome. The intervention was administered or tested for 12 weeks. The primary outcome was high-sensitivity C-reactive protein, with additional assessment of interleukin-6, HOMA-IR, triglycerides, waist circumference. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: Fermented turmeric milk reduced hs-CRP by 35.4% from 4.8 ± 1.2 to 3.1 ± 1.0 mg/L, whereas the control changed by 6.4% (between-group p<0.001). HOMA-IR decreased by 0.8 units and triglycerides by 22 mg/dL in the turmeric group; adherence was 93.2% and no serious adverse events occurred. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that fermented low-fat milk containing standardized turmeric and Lactobacillus culture could produce a meaningful improvement in high-sensitivity C-reactive protein through enhanced curcuminoid biotransformation during fermentation and modulation of inflammatory-metabolic signaling. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission.
Probiotic Fortification of Pearl Millet Beverage and Its Impact on Gut Microbial Diversity
Background: A shelf-stable non-dairy beverage may broaden access to microbiome-directed foods. Objective: To evaluate pearl millet beverage fortified with Lactobacillus plantarum and Bifidobacterium animalis in comparison with pasteurized non-probiotic pearl millet beverage using a randomized controlled microbiome trial. Methods: The draft protocol included 72 experimental units or participants involving healthy adults with low habitual fiber intake. The intervention was administered or tested for 8 weeks. The primary outcome was Shannon microbial diversity index, with additional assessment of Bifidobacterium relative abundance, fecal short-chain fatty acids, gastrointestinal symptom score, beverage acceptability. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: The probiotic pearl millet beverage increased Shannon diversity from 3.02 ± 0.31 to 3.46 ± 0.35, compared with a minimal change from 3.01 ± 0.29 to 3.08 ± 0.30 in controls (p<0.001). Bifidobacterium relative abundance increased by 3.9 percentage points and fecal butyrate by 28.4%, while gastrointestinal symptom scores remained low and beverage acceptability averaged 7.6 ± 0.7 of 9. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that pearl millet beverage fortified with Lactobacillus plantarum and Bifidobacterium animalis could produce a meaningful improvement in Shannon microbial diversity index through delivery of viable probiotics in a cereal matrix with fermentable millet substrates that support microbial cross-feeding. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission.
Cardiometabolic Benefits of Flaxseed-Enriched Chapati in Adults with Dyslipidemia
Background: The incorporation of flaxseed into a traditional staple can enhance adherence and relevance in populations over long-term. Objective: To compare two daily chapatis fortified with 20 g milled flaxseed to isocaloric standard wheat chapatis with the help of a randomized controlled trial. Methods: The protocol draft consisted of 90 experimental units or subjects of adults with untreated mild-to-moderate dyslipidemia. The intervention was given or subjected to 12 weeks. The main result was serum triglycerides, and secondary evaluation of LDL cholesterol, HDL cholesterol, systolic blood pressure, high-sensitivity C-reactive protein. Standard laboratory, clinical, or food-analysis methods were used and between-condition effects were tested at two-sided tests at α=0.05. Results: Flaxseed-enriched chapati reduced triglycerides by 45 ± 24 mg/dL compared with 7 ± 21 mg/dL after standard chapati (p<0.001). LDL cholesterol fell by 18 mg/dL, hs-CRP by 0.8 mg/L, and systolic pressure by 5.6 mmHg in the intervention group, with 92% adherence. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that two daily chapatis enriched with 20 g milled flaxseed could produce a meaningful improvement in serum triglycerides through alpha-linolenic acid, lignans, and viscous fiber acting on lipid metabolism, bile-acid handling, and vascular inflammation. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission.
Bioavailability of Iron from Moringa-Fortified Snack Bars in Adolescent Girls
Background: School-compatible snack may help support scalable strategies for adolescent ID. Objective: To compare the effect of a randomized controlled trial of daily snack bar fortified with Moringa oleifera leaf powder and ascorbic acid versus an energy-matched unfortified snack bar. Methods: The draft protocol comprised 100 experimental units/participants -- adolescent girls ages 13-18, who were low iron store but not severely anemic. Intervention/Testing Period: 12 weeks. Serum ferritin was the primary outcome and other parameters such as hemoglobin, transferrin saturation, soluble transferrin receptor, acceptability, and adherence was assessed. The standard laboratory, clinical, or food-analysis procedures were used and the two-sided tests at α=0.05 were used to determine between-condition differences. Results: Moringa-fortified snack bars increased serum ferritin from 18.6 ± 6.2 to 31.4 ± 8.1 ng/mL compared with a small rise to 21.0 ± 6.8 ng/mL in controls (p<0.001). The hemoglobin level rose by 0.9 ± 0.6 g/dL, transferrin saturation rose by 6.8 percentage points, and adherence was 90.6%, but biochemical iron excess did not occur in any participants. The direction and magnitude of the dummy findings was consistent within the primary and secondary endpoints and tolerability or assay-quality criteria were acceptable. Conclusion: The table showed that the daily snack bar fortified with Moringa oleifera leaf powder and ascorbic acid may be helpful in achieving a significant increase in the serum ferritin levels via the food based approach of iron supplementation and ascorbate mediated non-heme iron absorption. These findings are presented only as an example of a complete manuscript development, and prior to submission to scientific publications, should be replaced with ethically obtained and independently verified observations.
Satiety and Postprandial Glucose Responses to Chia-Seed Yogurt in Overweight Adults
Background: An easy snack change could help appetite control without the need for special supplements. Objective: Compare the efficacy of yogurt with 14 g of hydrated chia seed to plain yogurt with a randomized crossover trial, with the same caloric content. Methods: 40 experimental units or participants consisting of overweight adults with no diabetes were featured in the draft protocol. The intervention was administered or tested for three test visits separated by at least 5 days. The main outcome was 2-hour glucose area under the curve, and secondary tests included the visual-analogue satiety score, subsequent energy intake, peak glucose and gastric comfort score. The laboratory, clinical or food-analysis procedures were standardized and between-condition effects were assessed by two-sided tests at α=0.05. Results: Chia yogurt reduced the 2-hour incremental glucose area under the curve to 112 ± 24 mmol·min/L compared with 174 ± 31 mmol·min/L after plain yogurt (p<0.001). Peak glucose was 6.5 ± 0.7 versus 7.2 ± 0.8 mmol/L, while the test meal increased integrated satiety by approximately 27.9% and produced no serious intolerance. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: Conclusions: Based on the illustrative data set, the inclusion of 14 g of hydrated chia seed in a yogurt could result in a meaningful reduction in the 2-hour glucose AUC that may be attributable to the gel-forming properties of the seed, which increase viscosity and slow the nutrient delivery and thus extend the satiety. The findings presented here are to serve as merely an example of complete development of a manuscript and must be replaced with observations generated ethically and verified by the researcher before being submitted to scientific journals.
Development and Clinical Evaluation of a Low-Glycemic Jackfruit Flour Biscuit
Background: A portion-controlled shelf-stable product may translate jackfruit flour into a convenient clinical food. Objective: To evaluate biscuit containing 30% green jackfruit flour in comparison with macronutrient-matched wheat biscuit using a product-development and randomized crossover study. Methods: The draft protocol included 60 experimental units or participants involving adults with type 2 diabetes controlled by diet or metformin. The intervention was administered or tested for 4-week randomized crossover phase after product optimization. The primary outcome was postprandial glucose area under the curve, with additional assessment of glycemic index, fasting glucose, sensory acceptability, gastrointestinal tolerance. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: The optimized 30% jackfruit flour biscuit reduced acute glucose iAUC from 292 ± 39 to 184 ± 28 mmol·min/L and had an estimated glycemic index of 48 ± 6 (both p<0.001). After each four-week phase, fasting glucose was 7 mg/dL lower during the jackfruit biscuit period, while overall acceptability remained 7.5 ± 0.7 of 9 and gastrointestinal complaints were uncommon. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The biscuit made from the 30% green jackfruit flour might have a significant effect on the postprandial glucose area under the curve due to the presence of resistant starch, fibre and polyphenols, which delay the appearance of glucose and reduce the activity of enzymes in the gut. These are provided as a complete example of how to develop a manuscript and should be replaced before scientific submission with ethically collected, independently verified observations.
Effects of Pomegranate Peel-Enriched Bread on Antioxidant Status and Lipid Profile
Background: Valorization of pomegranate peel may yield a functional staple while reducing food-processing waste. Objective: To evaluate bread containing 5% pomegranate peel powder in comparison with matched wheat bread without peel powder using a randomized controlled trial. Methods: The draft protocol included 88 experimental units or participants involving adults with type 2 diabetes and stable medication. The intervention was administered or tested for 12 weeks. The primary outcome was total antioxidant capacity, with additional assessment of malondialdehyde, LDL cholesterol, triglycerides, high-sensitivity C-reactive protein. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: Pomegranate peel bread increased total antioxidant capacity from 1.18 ± 0.21 to 1.54 ± 0.24 mmol Trolox/L, versus 1.17 ± 0.20 to 1.21 ± 0.21 in controls (p<0.001). Malondialdehyde declined by 28.6%, LDL cholesterol by 17 mg/dL, and triglycerides by 21 mg/dL; adherence exceeded 91%. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The data in the example set indicates that bread enriched with 5% pomegranate peel powder may be a promising source of delivering ellagitannins and other polyphenols having an impact on redox balance, lipid oxidation, and inflammation, that could produce a meaningful enhancement in TAC. The results presented here are presented only as an illustration of the development of a manuscript; they must be replaced with ethically collected and independently confirmed observations in a scientific paper.
Synbiotic Coconut Water Beverage for Improving Bowel Regularity and Microbiome Composition
Background: A palatable synbiotic drink may offer a low-burden dietary approach to bowel regularity. Objective: To evaluate coconut water fermented with Lactiplantibacillus plantarum and supplemented with inulin in comparison with pasteurized coconut water without live cultures or inulin using a randomized controlled microbiome trial. Methods: The draft protocol included 80 experimental units or participants involving adults with functional constipation. The intervention was administered or tested for 8 weeks. The primary outcome was spontaneous complete bowel movements per week, with additional assessment of stool consistency, Bifidobacterium abundance, fecal short-chain fatty acids, gastrointestinal quality-of-life score. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: The synbiotic coconut water beverage increased spontaneous complete bowel movements from 2.1 ± 0.8 to 5.0 ± 1.4 per week, compared with an increase to 2.9 ± 1.1 in controls (p<0.001). Bifidobacterium abundance increased by 4.1 percentage points, fecal acetate and butyrate rose significantly, and gastrointestinal quality-of-life scores improved by 22.6%. Direction and magnitude of the dummy results were internally consistent between primary and secondary endpoints and tolerability or assay-quality standards were acceptable. Conclusion: The example data indicates that coconut water fermented with Lactiplantibacillus plantarum and inulin added may result in a significant change in spontaneous complete bowel movements per week with combined probiotic delivery and selective fermentation of inulin in a mineral-enriched beverage. These results are only meant as a thorough manuscript-development illustration and should be substituted with ethically produced, independently tested observations prior to being submitted to science.
Antihypertensive Effects of Beetroot and Amla Functional Drink in Prehypertensive Adults
Background: A complementary food-based formulation may target vascular tone through convergent mechanisms. Objective: To evaluate daily beetroot-amla functional drink standardized for nitrate and polyphenols in comparison with nitrate- and polyphenol-low placebo drink using a randomized controlled trial. Methods: The draft protocol included 96 experimental units or participants involving adults with untreated elevated blood pressure. The intervention was administered or tested for 8 weeks. The primary outcome was 24-hour systolic blood pressure, with additional assessment of 24-hour diastolic blood pressure, flow-mediated dilation, plasma nitrate/nitrite, oxidative stress index. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: The beetroot-amla drink reduced 24-hour systolic blood pressure by 7.7 ± 5.4 mmHg compared with 1.9 ± 4.8 mmHg after placebo (p<0.001). Flow-mediated dilation improved by 1.8 percentage points and plasma nitrate/nitrite increased 2.3-fold, with no clinically significant hypotension. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that daily beetroot-amla functional drink standardized for nitrate and polyphenols could produce a meaningful improvement in 24-hour systolic blood pressure through nitrate-derived nitric oxide combined with amla polyphenol-mediated protection of endothelial redox signaling. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission.
Omega-3-Enriched Plant-Based Laddus for Improving Triglyceride Levels in Vegetarian Adults
Background: A culturally acceptable snack may improve plant omega-3 intake in populations that do not consume fish. Objective: To evaluate two daily laddus enriched with flaxseed, chia, and walnuts in comparison with isocaloric conventional cereal-pulse laddus using a randomized controlled trial. Methods: The draft protocol included 92 experimental units or participants involving vegetarian adults with fasting triglycerides of 150-300 mg/dL. The intervention was administered or tested for 12 weeks. The primary outcome was fasting triglycerides, with additional assessment of non-HDL cholesterol, omega-3 index, high-sensitivity C-reactive protein, product acceptability. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: Plant omega-3 laddus reduced fasting triglycerides by 45 ± 27 mg/dL compared with 10 ± 24 mg/dL in controls (p<0.001). The omega-3 index increased from 3.4 ± 0.7% to 4.6 ± 0.8%, non-HDL cholesterol declined by 14 mg/dL, and acceptability remained above 7 of 9. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that two daily laddus enriched with flaxseed, chia, and walnuts could produce a meaningful improvement in fasting triglycerides through alpha-linolenic acid enrichment with fiber and polyphenols supporting hepatic lipid handling and systemic inflammation. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission.
Nutritional and Sensory Evaluation of Protein-Rich Sorghum and Chickpea Pasta
Background: A gluten-free cereal-pulse pasta can potentially enhance the protein density without compromising on the level of acceptability in cooking and sensory characteristics. Objective: To compare pasta prepared with 60% sorghum and 40% chickpea flour to commercial durum-wheat pasta based on a food formulation and sensory research. Methods: The experimental protocol had 75 experimental units or participants with trained sensory panel and untrained adult consumers. The intervention was tested or administered to study product-development. The main result was protein and other evaluation of dietary fiber, cooking loss, texture firmness, overall sensory acceptability. Standard laboratory, clinical or food-analysis methods were used, and between-condition effects tested using two-sided tests at α=0.05. Results: The sorghum-chickpea pasta with a 60:40 ratio contained 18.6 ± 0.7 g protein and 9.6 ± 0.6 g fiber, respectively, which are respectively increased by 53.7 percent and 200 percent relative to wheat control. The optimized pasta retained acceptable cooking loss (7.2 ± 0.6%) and achieved an overall sensory score of 7.6 ± 0.7, whereas the 50:50 formulation became excessively firm and less acceptable. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that pasta formulated with 60% sorghum and 40% chickpea flour could produce a meaningful improvement in protein content through complementary cereal-pulse amino-acid profiles and structural effects of legume protein on pasta quality. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission.
Effects of Resistant-Starch Rice on Postprandial Glycemia and Satiety
Background: A rice-based strategy may improve meal quality without requiring major changes in habitual cuisine. Objective: To evaluate high-amylose resistant-starch rice in comparison with conventional polished white rice matched for available carbohydrate using a randomized crossover trial. Methods: The draft protocol included 44 experimental units or participants involving adults with overweight and impaired fasting glucose. The intervention was administered or tested for two test visits separated by 7 days. The primary outcome was incremental glucose area under the curve, with additional assessment of insulin area under the curve, peak glucose, satiety area under the curve, subsequent energy intake. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: Resistant-starch rice reduced the 2-hour incremental glucose area under the curve to 166 ± 24 mmol·min/L compared with 282 ± 31 mmol·min/L after white rice (p<0.001). Peak glucose was 7.5 ± 0.7 versus 8.9 ± 0.8 mmol/L, while the test meal increased integrated satiety by approximately 27.6% and produced no serious intolerance. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that high-amylose resistant-starch rice could produce a meaningful improvement in incremental glucose area under the curve through reduced starch digestibility, delayed glucose appearance, and colonic fermentation of resistant starch. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission.
Vitamin D and Calcium Biofortification of Sesame-Based Nutraceutical Bites
Background: small sizes shelf-stable food can contribute to micronutrient sufficiency among low dairy consumers. Introduction: To compare fortified with non-fortified sesame based bites with calcium citrate and vitamin D3 through product-development and randomized feeding trial. Methods: The protocol was drafted with 84 experimental units or participants composed of healthy women under the age of 20-45 years having low levels of dietary calcium intake. The formulation testing was followed by 8-week randomized feeding phase of the intervention. The major product of the reaction was serum 25-hydroxyvitamin D and the secondary measure was the proxy of fractional calcium absorption, parathyroid hormone, sensory acceptability, product stability. Standardized laboratory, clinical or food-analysis methods were used and two-sided tests at 0.05 were used to test between condition effects. Results: Fortified sesame bites increased serum 25-hydroxyvitamin D from 17.8 ± 4.6 to 27.4 ± 5.2 ng/mL compared with a 0.9 ng/mL rise in controls (p<0.001). The parathyroid hormone level was reduced by 10 pg/mL, calcium-absorption proxy increased by 18.2 per cent and the product retained over 94% of vitamin D in eight weeks. Primary and secondary endpoint direction and magnitude of the dummy results were internally consistent and tolerability or assay-quality measures acceptable. Conclusion: The given example dataset indicates that a significant effect on serum 25-hydroxyvitamin D could be achieved by providing vitamin D and bioavailable calcium in a sesame-based bite fortified with calcium citrate and vitamin D3, which also delivers minerals and lignans. These results are only to be used as a full-fledged manuscript-development example and must continue to be substituted by ethically-produced independently verified observations prior to scientific submission.
Natural Product-Likeness Scores Revisited: Machine Learning Models for Predicting Drug-Like Properties from Phytochemical Databases
Natural products remain among the richest sources of drug leads, yet the cheminformatic tools used to prioritise molecules were largely developed with synthetic compounds in mind. Natural products occupy a distinct region of chemical space—characteristically higher in molecular weight, oxygen content, fraction of sp³ carbons and stereocentres, and lower in nitrogen, lipophilicity and aromaticity than synthetic drugs—and roughly one in five violate Lipinski's “Rule of Five” while remaining bioactive. This mismatch motivates a fresh look at the two families of scores that guide natural-product-based discovery: drug-likeness metrics (the Rule of Five, Veber, Ghose, Egan, Muegge and the quantitative estimate of drug-likeness, QED) and the natural product-likeness (NP-likeness) score of Ertl and colleagues, a Bayesian fragment-based measure that cleanly separates natural products from synthetic molecules. This review revisits these scores in the machine-learning era. We summarise the classical rules and their limitations for natural products; describe the NP-likeness score and its open-source and neural-network reimplementations; quantify the systematic physicochemical differences between natural-product and synthetic chemical space; survey the major phytochemical databases (COCONUT, LOTUS, NPASS, KNApSAcK) that now supply training data at scale; and examine how molecular representations (physicochemical descriptors, fingerprints such as ECFP/Morgan, and graph- and SMILES-based encodings) feed machine-learning models—random forests, gradient boosting, deep and graph neural networks—that predict drug-likeness, ADMET and bioactivity. We conclude that data-driven, natural-product-aware models are superseding rigid rule-based filters, but that their reliability depends on data quality, an honestly defined applicability domain, interpretability, and resistance to the historical bias toward synthetic, Rule-of-Five-compliant chemistry.
Retrobiosynthesis-Guided Discovery of Cryptic Secondary Metabolites: An AI-Assisted Reconstruction of Plant Metabolic Pathways
Plants synthesise an enormous chemical repertoire—more than 200,000 known specialised (secondary) metabolites and, by most estimates, a far larger number still undetected. Yet the biosynthetic pathways of the great majority remain unresolved, and many metabolites are “cryptic”: encoded in genomes but unexpressed under standard conditions, or predicted to exist but never isolated. This constitutes a vast reservoir of untapped chemistry of high therapeutic value, given that natural products underlie more than 70% of antibiotics and over half of anticancer agents. Conventional, gene-by-gene pathway elucidation cannot scale to this challenge—a difficulty compounded in plants, whose biosynthetic genes, unlike those of microbes, are frequently dispersed rather than clustered. This review examines an emerging solution: retrobiosynthesis, the application of retrosynthetic logic to enzyme-catalysed reactions, accelerated by artificial intelligence. We describe how AI-driven bio-retrosynthesis engines (READRetro, BioNavi-NP, graph-transformer models) predict biosynthetic routes backward from a target metabolite to primary-metabolite building blocks; how machine learning and genome mining (plantiSMASH, multi-omics ML) nominate the candidate genes and enzymes for each predicted step; and how protein-structure prediction (AlphaFold, ESMFold) and sequence-similarity networks refine enzyme-function assignment before heterologous expression validates the reconstructed pathway. We propose an integrated, iterative workflow uniting these components, illustrate it with a benzylisoquinoline-alkaloid example, and critically assess the principal limitations—enzyme promiscuity, data scarcity, plant genome complexity and the gap between prediction and experimental proof. Retrobiosynthesis-guided, AI-assisted reconstruction offers a scalable strategy for illuminating plant metabolic dark matter and unlocking cryptic secondary metabolites for drug discovery and synthetic biology.
Dietary Bioactive Compounds in Cancer Chemoprevention: Targeting Apoptosis, Angiogenesis, and Metastasis
Cancer chemoprevention—the use of natural or synthetic agents to prevent, delay or reverse carcinogenesis—has become an attractive complement to treatment, and diet is its most accessible instrument. Epidemiological and preclinical evidence consistently links diets rich in fruits, vegetables, spices and tea with reduced cancer risk, an effect attributed to their bioactive constituents: polyphenols, flavonoids, carotenoids, isothiocyanates and organosulfur compounds. This review examines how dietary bioactive compounds—curcumin, epigallocatechin gallate (EGCG), resveratrol, sulforaphane, quercetin, genistein, lycopene and anthocyanins—exert chemopreventive activity by targeting three interconnected hallmarks of malignancy: apoptosis, angiogenesis and metastasis. Acting across the initiation, promotion and progression stages of carcinogenesis, these compounds restore apoptotic competence (up-regulating pro-apoptotic Bax and caspases, down-regulating anti-apoptotic Bcl-2, and reactivating p53), suppress tumour angiogenesis (reducing VEGF and HIF-1α and microvessel density), and blunt metastasis (inhibiting matrix metalloproteinases MMP-2/9 and the epithelial–mesenchymal transition). Their defining feature is multi-target, pleiotropic action: a single dietary molecule typically engages all three processes, matching the multifactorial nature of cancer. Compiled data indicate 40–60% reductions in key angiogenic and metastatic markers in preclinical models, and several compounds—curcumin, EGCG, lycopene and folate among them—have advanced to clinical chemoprevention trials, albeit with variable results. The principal limitation, poor oral bioavailability, is increasingly addressed by nanoformulation. The review concludes that dietary bioactive compounds are rational, mechanistically grounded chemopreventive agents whose translation depends on standardisation, improved delivery and rigorous trials in defined high-risk populations.
Natural Products as Multi-Targeted Therapeutic Agents Against Chronic Inflammatory and Metabolic Disorders
Chronic inflammatory and metabolic disorders—type 2 diabetes mellitus (T2DM), obesity, non-alcoholic fatty liver disease (NAFLD/MASLD), atherosclerosis and the metabolic syndrome—are no longer viewed as separate entities but as manifestations of a shared pathophysiology termed “metaflammation”: a chronic, low-grade inflammatory state driven by over-nutrition that is inseparably coupled to metabolic dysregulation. Because this network is multifactorial and self-amplifying, single-target pharmacology is often insufficient, and there is growing interest in multi-targeted agents. Natural products—polyphenols, flavonoids, alkaloids, terpenoids and isothiocyanates such as berberine, curcumin, resveratrol, EGCG, quercetin, sulforaphane and silymarin—are archetypal polypharmacological molecules that simultaneously engage the master regulators sitting at the inflammation–metabolism interface: the energy sensor AMPK and the deacetylase SIRT1, the pro-inflammatory hub NF-κB and the NLRP3 inflammasome, the antioxidant transcription factor Nrf2, and the lipid-metabolic regulators PPARα/γ. This review integrates the mechanistic and clinical evidence for this class, mapping how individual compounds coordinate anti-inflammatory, insulin-sensitising, lipid-lowering and antioxidant actions. Pooled clinical data are substantial: berberine, for example, produces statistically significant reductions in fasting glucose, HbA1c, HOMA-IR and the inflammatory markers IL-6, TNF-α and CRP across meta-analyses of randomised trials, while curcumin, resveratrol and silymarin improve distinct cardio-metabolic parameters. The principal barrier to translation is poor oral bioavailability, which modern nanoformulation and delivery strategies are increasingly able to overcome. The review concludes that multi-targeted natural products are rational, evidence-supported candidates for the integrated management of metaflammation, provided their pharmacokinetic limitations and the need for standardisation and definitive clinical trials are systematically addressed.
From Traditional Medicine to Modern Oncology: Mechanistic Insights into Plant-Derived Anticancer Agents
The plant kingdom has been the single most productive source of clinically useful anticancer drugs, contributing an estimated 50–60% of the modern chemotherapeutic arsenal. What began as ethnomedical practice—the empirical use of medicinal plants across Ayurvedic, Chinese, and other traditional systems—was transformed, through mid-twentieth-century screening programmes, into four cornerstone drug classes: the vinca alkaloids (from Catharanthus roseus), the taxanes (from Taxus spp.), the camptothecin derivatives (from Camptotheca acuminata), and the epipodophyllotoxins (from Podophyllum spp.). This review traces that translational arc and dissects the molecular mechanisms that underpin it. The established agents act through precisely defined targets—vinca alkaloids destabilise and taxanes stabilise microtubules to arrest mitosis, while camptothecins and epipodophyllotoxins inhibit topoisomerases I and II, respectively, to trigger lethal DNA damage. A second, expanding wave of investigational phytochemicals—curcumin, resveratrol, EGCG, genistein, sulforaphane, withaferin A and others—acts pleiotropically across the hallmarks of cancer, inducing apoptosis (frequently p53-dependent), arresting the cell cycle, and simultaneously modulating the PI3K/AKT/mTOR, NF-κB, JAK/STAT3, Wnt/β-catenin and MAPK pathways, suppressing angiogenesis, metastasis and cancer-stem-cell survival. These compounds show micromolar in-vitro potency (representative IC₅₀ values of ~2–50 µM) and multitarget action with comparatively low toxicity, but their clinical translation is constrained by poor bioavailability and rapid metabolism. Modern strategies—nanoformulation (exemplified by nab-paclitaxel), semisynthesis, network pharmacology and rational combinations—are being deployed to close this gap. The review concludes that plant-derived agents remain central to oncology, and that integrating traditional knowledge with modern mechanistic and delivery science is the most promising route to the next generation of anticancer drugs.
Immunomodulatory Effects of Plant Polysaccharides in Chronic Inflammatory Diseases: From Innate to Adaptive Immunity
Chronic inflammatory and autoimmune diseases—including inflammatory bowel disease, rheumatoid arthritis, asthma and metabolic inflammation—arise from a failure of immune homeostasis in which innate over-activation and dysregulated adaptive responses reinforce one another. Plant polysaccharides, structurally complex biomacromolecules from herbs, fruits and fungi, have emerged as broad-spectrum immunomodulators that act, uniquely, along the entire innate-to-adaptive continuum. Recognised as pathogen-associated-molecular-pattern (PAMP)-like ligands by pattern-recognition receptors (PRRs)—chiefly Toll-like receptor 4 (TLR4), Dectin-1, the mannose receptor and complement receptor 3—they engage MyD88–NF-κB, Syk–Card9 and MAPK signalling to shape macrophage polarisation, dendritic-cell maturation and natural-killer-cell activity. Through dendritic-cell bridging they then direct the adaptive response, rebalancing the Th1/Th2, Th17/Treg and effector/regulatory axes whose distortion drives chronic inflammation. A third, indirect route operates through the gut microbiota: non-digestible polysaccharides are fermented to short-chain fatty acids that promote regulatory T-cell differentiation and epithelial-barrier integrity. This review synthesises these mechanisms, emphasising the structure–activity relationships—molecular weight, backbone linkage, branching and conformation—that govern potency, and the context-dependent, bidirectional nature of polysaccharide immunomodulation. Compiled preclinical data show 45–88% shifts in pro- versus anti-inflammatory mediators, restoration of Th17/Treg balance, and 45–62% reductions in disease-severity markers across models of chronic inflammation. The review concludes that plant polysaccharides are versatile homeostatic immunomodulators whose translation now depends on structural standardisation, resolution of oral-bioavailability limitations, and rigorous clinical validation.
Reversing Multidrug Resistance in Cancer Using Phytochemicals: Targeting Efflux Pumps and Survival Pathways
Multidrug resistance (MDR) is the principal cause of chemotherapy failure and is implicated in over 90% of deaths from advanced cancer. Its best-characterised driver is the overexpression of ATP-binding-cassette (ABC) efflux transporters—P-glycoprotein (P-gp/ABCB1), multidrug-resistance-associated protein 1 (MRP1/ABCC1) and breast cancer resistance protein (BCRP/ABCG2)—which actively extrude structurally diverse anticancer drugs and thereby lower their intracellular concentration. This transporter-mediated efflux acts in concert with dysregulated pro-survival and anti-apoptotic signalling (PI3K/AKT/mTOR, NF-κB, MAPK and Wnt/β-catenin), which both up-regulates the transporters and enables evasion of drug-induced apoptosis. Three generations of synthetic efflux-pump inhibitors have largely failed in the clinic because of intrinsic toxicity, poor selectivity and unfavourable pharmacokinetics. Phytochemicals—curcumin, resveratrol, quercetin, EGCG, tetrandrine, taxifolin, capsaicin, piperine and related natural products—have consequently emerged as a promising “fourth generation” of MDR reversers. This review synthesises their dual mechanism of action: direct functional inhibition and transcriptional down-regulation of ABC transporters on the one hand, and suppression of the survival pathways that sustain the resistant, apoptosis-evading phenotype on the other. Compiled data show reversal folds of roughly 3–15 relative to chemotherapy alone, two- to threefold increases in intracellular drug accumulation, and strong synergy in combination indices. Because most phytochemicals suffer from low aqueous solubility and poor systemic bioavailability, nanocarrier co-delivery systems—which additionally provide tumour targeting and reduce exposure of healthy P-gp-expressing tissues—are examined as the key translational enabler. The review concludes that phytochemicals, deployed within rationally engineered co-delivery platforms and combination regimens, represent a credible and comparatively non-toxic strategy for reversing cancer MDR, while highlighting the standardisation, pharmacokinetic and clinical-validation gaps that remain
Plant-Derived Natural Antimicrobials as Sustainable Alternatives to Synthetic Preservatives in the Food Supply Chain
Synthetic preservatives—sodium benzoate, potassium sorbate, sodium nitrite and the phenolic antioxidants BHA and BHT—underpin the safety and shelf life of the modern food supply, yet mounting toxicological scrutiny, tightening regulation and consumer rejection of chemical additives are eroding their acceptability; in 2026 regulators including the US FDA opened formal reassessments of BHA and BHT. Plant-derived antimicrobials offer a scientifically credible and sustainable alternative, combining broad-spectrum activity with renewable sourcing, biodegradability and high consumer acceptance. This review evaluates plant-derived antimicrobials as sustainable substitutes for synthetic preservatives across the entire farm-to-fork supply chain. Six compound classes—phenolics, terpenoid essential oils, organosulfur compounds, alkaloids, saponins and antimicrobial peptides—are examined with respect to chemistry, structure–activity relationships, antimicrobial spectrum and mechanism. Compiled minimum inhibitory concentrations (MICs) show that the principal actives carvacrol, thymol and cinnamaldehyde inhibit major foodborne pathogens (Escherichia coli, Salmonella, Listeria monocytogenes, Staphylococcus aureus) at roughly 128–400 µg/mL, with Gram-positive organisms generally more susceptible than Gram-negative. Their multi-target mode of action—membrane disruption, proton-motive-force collapse, reactive-oxygen-species induction, efflux-pump and quorum-sensing inhibition, and DNA binding—confers a low propensity for resistance, and binary combinations exhibit marked synergy, lowering effective doses by up to 75–94% and mitigating sensory impact. Supply-chain applications spanning pre-harvest treatment, processing, active packaging and storage are reviewed, with documented shelf-life extensions of two- to threefold. A sustainability assessment contrasts the renewable, low-ecotoxicity profile of plant antimicrobials with the cost, stability and regulatory advantages that still favour synthetics, and identifies the principal translational barriers—dose–sensory trade-offs, formulation stability, matrix effects, cost and regulatory harmonisation. The review concludes that plant-derived antimicrobials are best deployed not as one-for-one replacements but as engineered components of hurdle-based, clean-label preservation systems.
Natural Preservatives for Postharvest Spoilage Control: Efficacy, Mechanisms, and Industrial Scalability
Postharvest losses consume a substantial fraction of global fruit and vegetable production—commonly cited in the range of 20–40% for disease-driven decay and up to 28–55% overall—with microbial spoilage by fungi such as Botrytis cinerea, Penicillium spp., Rhizopus stolonifer and Monilinia spp. as the dominant biological driver, and an estimated global economic burden of the order of US$150 billion annually. Reliance on synthetic fungicides is increasingly constrained by pathogen resistance, regulatory withdrawal and consumer resistance to chemical residues, motivating a shift toward natural preservatives. This review provides an integrated, critical assessment of natural preservatives for postharvest spoilage control across three linked dimensions: efficacy, mechanisms and industrial scalability. Five principal classes—essential oils, polyphenol-rich plant extracts, microbial biocontrol agents, generally-recognised-as-safe (GRAS) compounds, and edible coatings—are compared with respect to their activity against the major postharvest pathogens. Reported decay reductions frequently reach 60–93%: for example, thyme and oregano essential-oil dips can lower grey-mould lesions in strawberries by roughly 70–90%, antagonistic yeasts and bacteria reduce disease incidence by 60–85% through competition and volatile organic compounds, and natamycin achieves up to ~93% control of citrus green mould. Mechanisms are shown to be multi-target—membrane disruption, enzyme and ATPase inhibition, reactive-oxygen-species induction, quorum-sensing and biofilm interference, nutrient and space competition, and elicitation of host defence—which underlies both broad-spectrum activity and a low propensity for resistance. Industrial translation is examined through registered commercial products (Bio-Save, Shemer, Nexy, BoniProtect, natamycin) and the barriers of cost, formulation stability, regulatory approval and supply-chain integration. Shelf-life extensions of two- to fourfold are documented across diverse commodities. The review concludes that natural preservatives are technically mature for several applications but require standardised efficacy testing, robust formulation, and clearer regulatory pathways to achieve wide commercial adoption.
Encapsulation Technologies for Plant Extracts: Enhancing the Efficacy of Clean-Label Food Preservatives
The global reformulation of processed foods toward “clean-label” compositions has intensified the search for natural preservatives capable of replacing synthetic antioxidants and antimicrobials such as butylated hydroxytoluene, nitrites, benzoates and sorbates. Plant extracts and essential oils rich in polyphenols, terpenoids and flavonoids are attractive candidates because they combine antioxidant and antimicrobial functionality with a favourable consumer perception. Their direct incorporation into foods is nonetheless constrained by chemical instability, volatility, poor aqueous solubility, pronounced sensory impact and limited bioavailability, which together erode preservative performance during processing and storage. Encapsulation has emerged as the principal technological strategy to overcome these barriers by entrapping bioactive compounds within protective carrier matrices, thereby improving stability, masking off-notes, enabling controlled release and permitting efficacy at lower effective doses. This review critically synthesises the state of the art in micro- and nanoencapsulation of plant-derived preservatives, comparing spray drying, freeze drying, complex coacervation, emulsion-based systems, liposomes, ionic gelation, molecular inclusion and electrohydrodynamic processing with respect to encapsulation efficiency, particle characteristics, cost and scalability. Clean-label constraints on wall materials are examined, and the mechanisms by which encapsulation enhances antioxidant and antimicrobial efficacy are analysed alongside release kinetics and mathematical modelling. Representative food-application case studies demonstrate shelf-life extensions of up to two- to fourfold relative to controls. Reported encapsulation efficiencies span roughly 54–99%, with nano-scale carriers frequently yielding the strongest antimicrobial gains at reduced doses. The review closes by outlining regulatory, safety and scale-up challenges and identifying research priorities—standardised reporting, digestion-relevant release testing and techno-economic assessment—required to translate encapsulated plant preservatives into commercial clean-label products.
Antibacterial Synergy between Curcumin and Standard Antibiotics against Multidrug-Resistant Staphylococcus aureus
Background: Combination therapy could lead to a decrease in the necessary exposure to antibiotics and enhance activity against resistant isolates. Purpose: To compare curcumin with oxacillin or vancomycin along with the investigation of curcumin against the antibiotics in a laboratory study. Methods: The protocol draft comprised 24 experimental units/participants with twenty four non-duplicate clinical isolates of multidrug resistant S. aureus. The test was conducted or given to determine its effects on its 48 hour in-vitro testing. The initial result was the reduction in minimum inhibitory concentration, and the further evaluation of the fractional inhibitory concentration index, biofilm biomass, time-kill log10 CFU/mL. Standardized laboratory, clinical, or food-analytical procedures were used and between-condition effects were tested using two-sided tests at 0.05. Findings: The curcumin–oxacillin combination decreased the median oxacillin MIC (128 µg/mL) to 16 µg/mL (eightfold), and 79.2% of the isolates attained the prespecified synergy threshold. At 24 hours, the combination reduced viable counts by 4.3 log10 CFU/mL relative to untreated growth control and decreased biofilm biomass to 28.6 ± 6.4% of control (both p<0.001). The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The example dataset suggests that curcumin combined with oxacillin or vancomycin could produce a meaningful improvement in minimum inhibitory concentration reduction through membrane disruption, interference with cell-division proteins, and restoration of antibiotic susceptibility. These findings are intended solely as a complete manuscript-development example and require replacement with ethically generated, independently verified observations before scientific submission. Keywords: curcumin; MRSA; antibiotic synergy; checkerboard assay; biofilm.
Renoprotective Potential of Boerhavia diffusa Root Extract in Cisplatin-Induced Nephrotoxicity
Background: A standardized botanical extract could provide a mechanistically plausible adjunct for limiting platinum-associated renal injury. Objective: To evaluate standardized Boerhavia diffusa root extract in comparison with cisplatin injury control and healthy control using a controlled animal experiment. Methods: The draft protocol included 40 experimental units or participants involving adult male Wistar rats. The intervention was administered or tested for 10 days. The primary outcome was serum creatinine, with additional assessment of blood urea nitrogen, renal malondialdehyde, superoxide dismutase, tubular injury score. Standardized laboratory, clinical, or food-analysis procedures were applied, and between-condition effects were evaluated with two-sided tests at α=0.05. Results: Cisplatin increased serum creatinine to 2.31 ± 0.34 mg/dL; high-dose Boerhavia diffusa limited the increase to 0.96 ± 0.14 mg/dL (p<0.001 versus injury control). The high-dose extract lowered renal malondialdehyde by 54.4%, restored superoxide dismutase activity to 34.1 ± 4.0 U/mg, and reduced the composite tubular injury score from 8.7 ± 1.0 to 2.2 ± 0.8. The direction and magnitude of the dummy findings were internally consistent across primary and secondary endpoints, and tolerability or assay-quality criteria remained acceptable. Conclusion: The sample data indicates that standardized Boerhavia diffusa root extract may lead to significant enhancement of serum creatinine with antioxidant preservation, lipid peroxidation suppression, and tubular inflammation. The findings are only to be taken as a complete manuscript-development model, and should be substituted with ethically created, self-confirmed findings prior to scientific presentation. Keywords: Boerhavia diffusa; cisplatin; nephrotoxicity; oxidative stress; renoprotection.
Analgesic and Anti-Inflammatory Activity of Boswellia serrata Resin Fractions in Experimental Arthritis
Background: Through fraction-guided analysis, it is possible to determine a reproducible resin preparation that has a symptomatic and tissue-level response. Objective: To evaluate boswellic-acid-rich resin fraction in comparison with vehicle control and diclofenac reference treatment using a controlled animal experiment. Methods: 48 experimental units or participants that involved adult Wistar rats with complete Freund adjuvant arthritis were contained in the draft protocol. The 21 days of intervention/testing. The main outcome was paw edema, and secondary measurements of mechanical pain threshold, arthritis score, serum TNF-a, histologic synovitis score. Conventional laboratory, clinical, or food-analysis methods were used and between-condition effects were analyzed using two-sided tests at α=0.05. Results: High-dose boswellic-acid-rich fraction reduced paw edema from 1.62 ± 0.22 mL in arthritis controls to 0.67 ± 0.15 mL and increased mechanical pain threshold from 34 ± 6 g to 63 ± 8 g (both p<0.001). The identical fraction elevated TNF-a by half and enhanced the compound histologic score of 9.9 ± 1.1 to 3.6 ± 0.9which was nearly equivalent to diclofenac reference response. There was internal consistency between the direction and magnitude of the findings of the dummy between the primary and the secondary endpoints and the tolerability or assay-quality criteria was also acceptable. Conclusion: The given example data indicate that boswellic-acid-rich resin fraction might lead to a significant change in paw edema via the 5-lipoxygenase modulation and the drop in pro-inflammatory cytokine signaling. These results are meant purely as a full-manuscript-development illustration and would need substitution with ethically produced independently-validated observations prior to submission to science. Keywords: Boswellia serrata; arthritis; analgesic; inflammation; boswellic acids.
Cytoprotective Effects of Emblica officinalis Polyphenols Against Heavy-Metal-Induced Oxidative Damage
Background: Defined polyphenol fractions could provide a quantifiable method of studying environmental toxicant protection. Purpose: To compare polyphenol-enriched Emblica officinalis fraction with untreated and exposed-to-metal cells through a cell-culture experiment. Procedures: The protocol draft had 6 experimental participants or units that involved human HepG2 hepatocyte-like cells that were exposed to sodium arsenite. It was tested and/or administered over 24 hours. The main outcome was cell viability, and further detecting intracellular reactive oxygen species, malondialdehyde, reduced glutathione, caspase-3 activity. Laboratory, clinical or food-analysis procedures were used as standardized and between-condition differences compared by two-sided tests at 0.05. Findings: Cell viability was lowered to 52 ± 6% by arsenite; the viability was maintained at 92 ± 4% by 100 µg/mL Emblica polyphenols pretreatment; lactate dehydrogenase release was lowered to 14 ± 3% (p<0.001). The highest concentration lowered intracellular reactive oxygen species from 286 ± 31% to 119 ± 14% of control and reduced caspase-3 activity from 4.6 ± 0.6-fold to 1.4 ± 0.2-fold. Direction and magnitude of the dummy results were internally consistent between primary and secondary endpoints, tolerability or assay-quality criteria were acceptable. Conclusion: The given example dataset indicates that polyphenol-containing Emblica officinalis fraction might result in a significant increase in cell viability due to radical-scavenging, glutathione preservation, and mitochondria-related apoptosis reduction. These results are simply to be used as a full example of manuscript development and must be substituted with independently validated, ethically produced observations prior to being submitted in a scientific paper. The keywords are to be Emblica officinalis, polyphenols, arsenic, oxidative stress, and cytoprotection.
Electrochemical Biosensors for Rapid Detection of Pesticide Residues in Fresh Produce: A Review of Nanomaterial-Enhanced Platforms
Pesticides residues in fresh produce is an important food safety issue as repeated exposure through the diet can cause neurological, reproductive, endocrine, developmental and carcinogenic effects. Traditional chromatographic techniques are reliable for the identification and quantification of pesticides, but they are costly, need specialized staff, labor-intensive sample preparation and centralized laboratory. The present review explores the electrochemical biosensors based on nanomaterials as rapid, sensitive, portable and cost-effective alternatives for pesticide-residue screening. Metal nanoparticles, metal oxides, carbon nanomaterials, conducting polymers, metal–organic frameworks, two-dimensional materials and hybrid nanocomposites enhance the conductivity of the electrodes, surface area, immobilization of bioreceptors, catalytic activity, and signal amplification. Critically discussed are enzyme-based biosensors, immunosensors, aptamer-based sensors, molecularly imprinted polymer sensors and non-enzymatic platforms developed for organophosphates, carbamates, organochlorines, pyrethroids and neonicotinoids. Sample preparation, matrix effects, electrode fouling, sensitivity, selectivity, detection limits, recovery, reproducibility and comparison with chromatographic reference methods are also assessed in the review. Decentralized monitoring might be possible with emerging screen-printed electrodes, paper-based devices, microfluidic systems, smartphone interfaces, connectivity to the Internet of Things, and artificial intelligence-assisted signal analysis. Yet, there are significant challenges towards stability and safety of sensors, reproducible manufacture of nanomaterials, multiplexing, regulatory validation, and commercialization. To realize these platforms into reliable food-safety tools, standardized fabrication, use of sustainable materials, real-sample validation, and interlaboratory evaluation are key.
Synergistic Antimicrobial Efficacy of Essential Oil Combinations Against Foodborne Pathogens: A Network Pharmacology Approach
Foodborne pathogens remain a significant public health concern and a challenge for the global food industry, leading to widespread illness, deaths, and substantial economic costs. Rising antimicrobial resistance and increasing consumer preference for natural preservatives have driven research into plant-based essential oils (EOs) as viable, sustainable alternatives to synthetic antimicrobials. Evidence shows that combining essential oils can produce synergistic effects, improving their ability to suppress harmful microbes at lower doses while reducing negative impacts on food flavor and aroma. This review explores the combined antimicrobial effectiveness of essential oil mixtures against key foodborne pathogens such as Escherichia coli, Salmonella enterica, Listeria monocytogenes, Staphylococcus aureus, and Bacillus cereus. Special attention is given to network pharmacology—an emerging systems biology method—that helps uncover the complex interactions among multiple components, targets, and biological pathways involved in the antimicrobial action of EO combinations. The analysis covers major bioactive compounds in common essential oils, their molecular targets, and their roles in disrupting microbial membranes, inducing oxidative stress, inhibiting biofilm formation, interfering with quorum sensing, and altering metabolic functions. It also summarizes recent progress in protein–protein interaction networks, Gene Ontology enrichment, KEGG pathway analysis, and compound–target mapping to better understand how synergy enhances antimicrobial activity. Additionally, the review highlights practical applications of synergistic EO formulations in food preservation techniques like active packaging, nanoencapsulation, and edible coatings, while addressing existing hurdles such as variability in composition, standardization, safety evaluation, and regulatory compliance. Looking ahead, the integration of artificial intelligence, multi-omics approaches, molecular docking, and network pharmacology is discussed as a promising path toward designing more effective, science-based natural antimicrobial solutions. Overall, this work offers a structured approach to developing advanced, synergistic essential oil strategies that improve food safety, prolong shelf life, and help counteract antimicrobial resistance in contemporary food systems.
From Kitchen to Clinic: Translational Evidence for Functional Foods in Chronic Disease Management
Functional foods have gained attention as effective dietary strategies for preventing and managing chronic illnesses due to their abundance of bioactive compounds such as polyphenols, dietary fibers, probiotics, omega-3 fatty acids, carotenoids, and bioactive peptides. Increasing scientific research indicates that these elements provide antioxidant, anti-inflammatory, immunomodulatory, and metabolic regulatory benefits, which help in lowering the risk and progression of significant chronic conditions like cardiovascular disease, type 2 diabetes mellitus, obesity, cancer, neurodegenerative disorders, and gastrointestinal diseases. This review delves into the evolution of functional foods from traditional dietary practices in kitchens to their application in evidence-based clinical settings. It emphasizes the molecular mechanisms that contribute to their health benefits and critically assesses findings from both preclinical and clinical studies that support their therapeutic potential. Additionally, the review addresses major obstacles to clinical translation, such as variations in bioactive composition, limited bioavailability, lack of standardization, regulatory inconsistencies, and the necessity for large-scale randomized clinical trials. The review also explores emerging advancements in precision nutrition, gut microbiome research, multi-omics technologies, artificial intelligence, and digital health as transformative methods for enhancing functional food interventions and facilitating personalized dietary strategies. By combining mechanistic insights with clinical evidence and future technological advancements, this review offers a comprehensive view of the role of functional foods in managing chronic diseases and identifies research priorities essential for their integration into routine healthcare and public health nutrition.
Bioactive Peptides and Polyphenols as Next-Generation Nutraceuticals: Mechanisms, Bioavailability, and Clinical Prospects
Bioactive peptides and polyphenols are compounds found in food that have complementary molecular actions that could participate in the prevention or in the complementary treatment of chronic pathologies. The peptides derived from the proteins in dairy, marine, egg, plant, and meat products can alter the activity of angiotensin-converting enzyme, redox homeostasis, glucose metabolism, immune signalling, and satiety, while polyphenols are known to regulate antioxidant-response pathways, inflammatory mediators, cellular metabolism, vascular function, and gut microbial ecology. They have been limited in their translation to effective nutraceuticals, however, due to inconsistent compositions, gastrointestinal degradation, restricted intestinal permeability, fast metabolism, unknown tissue exposure, and inconsistent clinical trials. This critical integration of evidence brings together sources, structure–activity relationships, production, molecular mechanisms, pharmacokinetics, formulation technologies, clinical applications, safety and regulation information in the context of this narrative review. Literature was retrieved from PubMed/MEDLINE, Scopus, Web of Science and Google Scholar by employing various combinations of the keywords bioactive peptides, polyphenols, nutraceuticals, bioavailability, nanoencapsulation, mechanisms and clinical trials. There is current evidence for biologically plausible effects on antioxidant, anti-inflammatory, cardiometabolic, neuroprotective, and microbiota-mediated effects, but results in purified systems or animal models do not necessarily translate to human exposures. Apparent bioavailability and stability may be improved by encapsulation, lipid-based carriers, protein–polysaccharide complexes, cyclodextrins, and co-delivery systems, but there is limited comparative human data. Future developments should focus on chemically standardized preparations, validation of dose-response biomarkers, dose-ranging studies, interaction testing and sufficiently sized studies based on diet, microbiome, metabolic phenotype and medication use. Bioactive peptides and polyphenols are next-generation nutraceutical ingredients, but with clinically credible products, there needs to be a change from general antioxidant claims to evidence that is specifically linked to the mechanism, formulated to a specific product, and compliant with regulatory standards.
Biodiversity-Driven Functional Food Innovation: Harnessing Underexplored Native Crops for Human Health
Highly vulnerable to climate change, biodiversity loss, malnutrition, and supply chain disruptions, global food systems rely on a few staple crops. Underexplored native crops such as minor millets, native legumes, pseudocereals, traditional leafy vegetables, roots and tubers, oilseeds and fruits with climate resilience are valuable raw materials for developing sustainable functional foods. The current review discusses the potential of crop biodiversity in functional food innovation and its impact on human health. Native crops are rich in a variety of high-quality proteins, complex carbohydrates, dietary fibre, resistant starch, essential fatty acids, vitamins, minerals, polyphenols, carotenoids, phytosterols, bioactive peptides, and other specialized phytochemicals. These constituents can have antioxidant, anti-inflammatory, antidiabetic, cardioprotective, lipid-regulatory, immunomodulatory, neuroprotective, and gut-microbiome-modulatory effects. Their biological activity is, however, genotype-dependent, culture media-dependent, dependent on maturity, processing, interactions with food matrix, bioaccessibility, and individual host factors. The nutritional quality, sensory acceptability, stability, safety and bioavailability are expected to be enhanced by conventional and emerging technologies, such as germination, fermentation, extrusion, high-pressure processing, ultrasound-assisted extraction, freeze drying and microencapsulation. Several factors remain to be addressed to expand the use of commercial translation, including limited compositional databases, inconsistent standardization, lack of clinical evidence, poor seed and processing systems, regulatory uncertainty, and low consumer awareness. Future innovations need to combine conservation and knowledge of crop varieties, standardized nutritional profiling, sustainable processing, clinical validation, health claims (with responsibility), equitable benefit sharing, and inclusive value chain development. Utilization of under-exploited local crops can diversify nutrition, increase food system resilience, boost rural livelihoods, preserve agrobiodiversity, and facilitate production of functional foods with proven benefits for eating habits and public health, which helps in the prevention of chronic diseases.
Biogenic Synthesis of Selenium Nanoparticles: Structure-Activity Relationships and Antioxidant Mechanisms
Selenium nanoparticles (SeNPs) have emerged as redox-active nanomaterials that combine the biological relevance of selenium with size-dependent physicochemical properties. This review examines biogenic SeNP production, the relationships between structural attributes and biological performance, and the mechanisms underlying antioxidant activity. Plants, bacteria, fungi, yeasts, algae, and isolated biomolecules can reduce selenium oxyanions to elemental selenium while simultaneously generating a surface corona that influences nucleation, growth, colloidal stability, cellular interactions, and toxicity. Synthesis variables—including pH, temperature, reaction time, precursor concentration, extract composition, and biomass ratio—govern particle size, morphology, crystallinity, charge, aggregation, and dissolution. These characteristics determine protein adsorption, membrane interaction, cellular uptake, intracellular selenium availability, and ultimately the balance between antioxidant and pro-oxidant effects. At appropriate exposures, biogenic SeNPs may support glutathione peroxidase and thioredoxin reductase systems, increase glutathione, activate Nrf2–Keap1–ARE signaling, limit reactive oxygen and nitrogen species, preserve mitochondrial function, and reduce lipid, protein, and DNA oxidation. Reported applications include protection in metabolic, inflammatory, neurodegenerative, hepatic, renal, and reproductive models, as well as antimicrobial and anticancer strategies in which controlled oxidative stress may be desirable. Translation remains limited by inconsistent biological starting materials, incomplete nanoparticle characterization, variable dose reporting, insufficient pharmacokinetic evidence, and a lack of standardized long-term safety studies. Future progress requires quality-by-design synthesis, reference materials, mechanistically linked structure–activity datasets, physiologically relevant models, and transparent reporting. Biogenic SeNPs are promising antioxidant platforms, but their clinical value depends on reproducible manufacture and exposure-specific safety evaluation.
Life-Cycle Assessment and Toxicity Profiling of Green Nanomaterials: Bridging Sustainability and Biomedical Safety
The production of green nanomaterials may utilize renewables as feedstocks, biological reducing agents, aqueous processing, or minimize the use of hazardous reagents, which makes them potentially useful for drug delivery, imaging, diagnostics, tissue engineering, antimicrobial systems, and regenerative medicine. But green synthesis does not necessarily ensure life-cycle sustainability or biomedical safety. This review combines the concepts of life-cycle assessment (LCA), physicochemical characterization, exposure analysis, and toxicity profiling in order to facilitate safe-and-sustainable-by-design development. LCA establishes the functional unit, system boundary, lists material and energy flows, and quantifies burdens related to the acquisition, synthesis, purification, formulation, use, release, recycling, and disposal of feedstock. Toxicity profiling includes the study of size, morphology, composition, crystallinity, surface chemistry, aggregation, dissolution, protein-corona formation, delivered dose, cellular uptake, biodistribution, persistence, and clearance. These endpoints are related to cytotoxicity, oxidative stress, genotoxicity, inflammation, hemocompatibility, immunotoxicity, organ toxicity, reproductive effects and ecotoxicity. Integration is important since reducing energy and/or solvent consumption can affect purity, stability, dose, effectiveness, or toxicity; and safer surface modifications can add to processing requirements. The use of multi-criteria decision analysis to identify trade-offs, combined with a framework that compares functionally equivalent alternatives, quantifies uncertainty and applies mechanistically relevant models, should be used. The challenges are the incompleteness of the inventory, the lack of consistency in how the dose is measured, biological batch variability, assay interference, lack of long-term studies and inadequate end-of-life transformation data. Quality-by-design manufacturing, harmonized reporting, reference materials, high throughput and new approach methodologies, toxicokinetic modeling, and regulatory alignment are some of the items needed for future translation. When connected to toxicity evidence, LCA can avoid burden shifting and facilitate the development of green nanomaterials to make them good for both the environment and society, and effective for clinical applications.
From Ethnobotany to Evidence: Integrative Strategies for Phytochemical Discovery and Therapeutic Translation
Ethnobotanical knowledge has been used to discover clinically useful natural products many times, but the conversion from traditional use to documented reproducibility of therapeutic benefit is inefficient. This review presents an integrative approach that links community-based knowledge to the botanical validation, contemporary extraction, chemical characterisation, bioactivity testing, multi-omics, network pharmacology, artificial intelligence, drug formulation and clinical validation. The suggested framework sees the discovery of phytochemicals not as a linear process looking to one single active ingredient, but as a process of generation evidence in an iterative manner. Specific attention is given to surveillance and documentation of plant species and their use, orthogonal analytical confirmation, mechanism-dependent screening, mechanism-based drug pharmacokinetics and mechanism-based toxicity characterisation, and early focus on product standardisation. Computational approaches can be useful to speed up the annotation and prioritisation process, but the predictions of these approaches should be tested experimentally and reported openly. Other factors include fit-for-purpose formulations, well-designed clinical trials, pharmacovigilance, ethical access and benefit sharing, and sourcing sustainability. Combination of all these can lead to lesser false-positive discoveries, greater reproducibility and distinguish promising phytochemicals from insufficiently characterised extracts. Thus, a coordinated link from ethnobotany to evidence may be beneficial for natural product drug discovery and for standardizing and evidence-based phytomedicines.
Beyond Isolation: Modern Analytical and Computational Approaches to Unraveling Plant Metabolomes
Plant metabolomes consist of a complex and dynamic mixture of small molecules, which are chemically diverse and spatially organized, and that link the genotype, environment, development, and phenotype. While conventional phytochemical isolation is important for structural confirmation, its compound-by-compound workflow doesn't comprehensively capture this complexity. This review discusses the analytical and computational shift from targeted isolation to integrated plant metabolomics. The robust profiling of primary metabolites by gas chromatography–mass spectrometry is complemented by the ability of liquid chromatography–high-resolution mass spectrometry to profile lipids and specialized metabolites. Nuclear magnetic resonance spectroscopy provides quantitative reproducibility and structural information, and capillary electrophoresis, ion mobility, and mass-spectrometry imaging provide chemical variation information for ions, isomers, and spatial information. Reliable discovery requires good experimental design, rapid quenching, representative extraction, pooled quality-control samples, internal standards, and transparent metadata. Computational workflows allow raw signals to be transformed into aligned features, correct analytical drift, prioritize molecular formulae, match spectral library, simulate fragmentation, construct molecular network, and integrate statistical learning and pathway and multi-omics evidence. There are many challenges that persist, such as limited metabolome coverage, ion suppression, batch effects, unrecognized features, ambiguous feature annotation, poor cross-study comparability, and lack of reference standards. Curated training data, reporting of uncertainties, chemical validation, and the FAIR deposition of data are needed for emerging spatial and single-cell methods, field-deployable instrumentation, machine learning, foundation models and automated laboratories to facilitate discovery. Complementary platforms, coupled with evidence-aware computation and biological-specific confirmation, can enable the translation of multidimensional signals to reproducible metabolite identities, biomarkers, pathways and causal hypotheses. This integrated approach enables crop improvement, stress biology, food quality, chemotaxonomy, nutrition and natural product discovery.
Traditional Knowledge Meets Molecular Networks: A Systems-Level Re-evaluation of Medicinal Plant Bioactivity
Traditional medicinal knowledge has played a significant role in the identification of bioactive plants through the many times it has led to the identification of those plants, however, there have been many times when traditional evaluation turned into isolated compounds and single targets. The review revisits the bioactivity of medicinal plants by systematically linking ethnobotanic knowledge, phytochemical diversity, molecular networks, multi-omics information, computational prediction and experimental validation. The relationship among constituents, targets, pathways, diseases, and phenotypes can be mapped using network pharmacology; the high throughput of metabolomics, transcriptomics, and proteomics, molecular docking, and machine learning can provide mechanistic plausible leads for the prioritisation. But network diagrams are models, not realities; authenticated plant material, quantitative chemical standardisation, exposure responsive target assessment, orthogonal assays, causal perturbation, reproducibility, pharmacokinetics and clinically relevant outcomes are all necessary to achieve reliable translation. Whole-extract effects cannot be inferred from individual compounds, as effects may be additive, synergistic, antagonistic or via bioavailability modification. Technical innovation should be accompanied by ethical governance, protection of indigenous knowledge, benefit sharing, biodiversity conservation and quality standards for regulations. The computational-experimental cycle can largely retain the context of traditional knowledge whilst transforming complex botanical preparations into clinical, reproducible and testable therapeutic hypotheses.
Circadian Phytonutrition Using Polyphenol-Rich Botanical Extracts to Remodel Immunometabolism in Autoimmune Thyroid Disorders: A Mechanistic Review and Research Roadmap
Imbalances between proinflammatory T helper 17 (Th17) cells and immunosuppressive regulatory T (Treg) cells have been shown to play a key role in the pathogenesis of autoimmune thyroid diseases (AITD), such as Hashimoto's thyroiditis and Graves' disease. In particular, Th17/Treg cell ratio is associated with levels of autoantibodies and the severity of AITD. Furthermore, different metabolic pathways underlie the generation of Th17 cells (glucose-dependent anabolic metabolism) and Tregs (oxidative mitochondrial metabolism). The association between these Th17/Treg immune cells, their respective metabolic pathways and the autoimmune disease has led to the hypothesis that AITD is partly an immunometabolic disease. Moreover, it has been demonstrated independently that molecular clocks of thyroid cells are altered in autoimmune thyroiditis, with decreased expression of circadian rhythm proteins BMAL1 and PER2, which correlate with the level of inflammation and that disruption of circadian rhythm (light-shift) exacerbates the disease severity in the animal model. Third line of research, not linked to either of the above, has revealed the ability of various dietary polyphenols — such as green tea catechins (EGCG), curcumin, resveratrol, citrus polymethoxyflavones — to modulate transcription of the core clock-genes and directly inhibit Th17 development while enhancing Treg induction. This review integrates these three bodies of literature and offers, in the form of an explicit but untested hypothesis, that timing of polyphenol administration from botanical sources in alignment with, rather than disregard of, the physiological rhythms of the intrinsic immune and thyroid clocks will be capable of reprogramming Th17/Treg metabolism in early AITD more effectively than conventional polyphenol ingestion. It refer to this strategy as circadian phytonutrition. The review concludes with a series of in vitro, animal and human studies in translation, designed as a step-wise process. No original data are offered here; all proposed mechanisms are hypotheses derived from indirect evidence.
Natural Carbon Quantum Dots Derived from Medicinal Fruit Wastes for Early Fluorescence-Guided Detection and Suppression of Pancreatic Fibrosis: A Mechanistic Review and Research Roadmap
Fibrosis of the pancreas resulting from the activation of PSCs to acquire a myofibroblast-like state characterized by secretion of collagen contributes both to chronic pancreatitis and the development of desmoplastic stroma associated with pancreatic cancer; however, this is hard to diagnose due to inaccessibility of the pancreas for biopsy procedures and inadequacy of currently available imaging methods that are unable to detect early stages of fibrotic lesions preceding symptoms. Fruit and medicinal plant wastes like citrus peel, grape pomace, mulberry, mango leaf, and others serve as a raw material to produce carbon quantum dots (CQDs), which have been independently found to exhibit tunable fluorescence and photostability required for bioimaging applications as well as antioxidant properties and ability to scavenge ROS due to their surface hydroxyl and carbonyl groups. The following two direct precedents set the context for this review: fruit waste-derived CQDs coupled with antibodies against a pancreatic cancer biomarker CA 19-9 have already been employed for fluorescent immunoassay detection purposes, while in a completely different study carbon dots derived from mulberry were proven to inhibit kidney fibrosis in vivo by blocking PI3K pathway. As far as it can ascertain, no previous research has attempted to integrate these two lines of evidence in a single CQD-based approach. Specifically, no work has been done that would combine these two approaches into one carbon quantum dot-based technology, or would test the efficacy of such a technology in the treatment of the pancreatic tissue. The current review integrates the biological aspects of the process of pancreatic stellate cells activation, green chemistry of carbon quantum dots based on fruit waste, and the recent evidence of their potential in the anti-fibrotic activity in organs, suggesting an explicit, yet untested hypothesis that natural CQDs could be used to detect and prevent early stages of pancreatic fibrosis. A three-stage in vitro, animal and translational roadmap concludes this manuscript.
Microbiome-Engineered Herbal Synbiotics for Regulating Ferroptosis and Mitochondrial Dysfunction in Age-Related Sarcopenia: A Mechanistic Review and Research Roadmap
Sarcopenia – the age-associated progressive decline in muscle mass, strength, and functionality, predicted to impact over 200 million elderly people on the planet in four decades – has the following two converging cellular mechanisms of development: ferroptosis – an iron-mediated, lipid peroxidation-induced form of cell death recently shown to occur in sarcopenic mouse muscles by virtue of p53/SLC7A11/GPX4 pathway; and progressive mitochondrial malfunction, manifested by compromised mitochondrial biogenesis, defective mitophagy, and reduced oxidative activity. The two aforementioned cellular mechanisms are interrelated: dysfunctional mitochondria and mitochondrial fission lead to ferroptotic lipid peroxidation, and the latter results in further damage to the mitochondria – hence a vicious cycle mechanistically proven to exist in non-skeletal muscle tissue. In addition, there is quite a lot of evidence of the presence of gut-muscle axis, where certain microbial taxa of the gut, as well as short-chain fatty acids produced by them, determine muscle mass and functionality, whereas several plant-derived phytometabolites – curcumin, tannic acid, polydatin, and catechin among them – inhibit ferroptosis through the same p53/SLC7A11/GPX4 and Nrf2 pathway. The current review aims to hypothesise that engineered synbiotics (probiotics in combination with anti-ferroptosis herbal phytometabolites encapsulated in a plant-based matrix for targeted delivery to the colon, which is a technology that has already been validated in the case of another metabolic disease) would be capable of correcting age-related dysbiosis and delivering anti-ferroptosis and mitochondria protective phytometabolites to the skeletal muscles, thereby addressing both parts of the sarcopenia-driving mechanism via the single formulation. This strategy will be called microbiome-engineered herbal synbiotics. The review ends with a stepwise roadmap including in vitro, animal, and initial clinical translation stages. No novel experimental findings are provided; all the proposed links between mechanisms are formulated as hypotheses based on the existing evidence.
Plant Extracellular Vesicle-Encapsulated Phytometabolites for Reversing Cellular Senescence Associated with Chronic Kidney Disease: A Mechanistic Review and Research Roadmap
CKD is characterized by the presence of senescent tubular epithelial cells in increasing numbers in association with the injury of the nephrons, and the secretory phenotypes associated with these senescent cells are responsible for the fibrosis seen during CKD progression. Natural small molecule senotherapy agents (senolytics and senomorphics) obtained from plant sources like quercetin, fisetin, and curcumin have demonstrated biological activity towards senescent cells, and one senolytic combination drug has even been used in a clinical trial in diabetic kidney disease patients. Despite the biological activity of these drugs, they lack aqueous solubility, fast systemic clearance, and renal bioavailability as free molecules. Extracellular vesicles from edible plants such as ginger, grape, and grapefruit – plant-derived extracellular vesicles (PEVs) – have been proposed as another delivery vehicle for senotherapeutic drugs, providing natural biological activity, good biocompatibility, low manufacturing cost, and ability to load phytometabolite drugs like curcumin. This review summarizes the physiology of cellular senescence in CKD, the evidence for plant-based compounds as senotherapeutic agents, and the rationale behind using PEVs to deliver the drugs. Three areas of literature that have so far been studied largely in isolation are brought together to construct a five-point hypothesis about how PEV-encapsulated plant metabolites can mitigate renal cellular senescence. The review ends with a proposed step-by-step approach to translating the hypothesis into in vitro and animal studies before human trials. There is no experimental data provided here; each proposed link in the mechanistic chain is a hypothesis formed based on indirect evidence.
Postbiotic Metabolites from Indigenous Fermented Medicinal Plants as Candidate Modulators of the Gut–Retina Axis in Early Diabetic Retinopathy: A Mechanistic Review and Research Roadmap
Approximately a quarter of the world’s diabetics suffer from DR, with 160 million patients anticipated by 2045, but there is no effective treatment beyond glycemic control for its earliest, non-proliferative form. In the last five years, another area of research has developed a view of a “gut-retina axis,” where gut dysbiosis, gut barrier dysfunction, and circulating bacterial metabolites contribute to retinal inflammation and vascular biology in the same way that the gut-brain axis influences neurological outcomes. Simultaneously, the notion of postbiotics, defined as inactive microorganisms and/or components with proven health benefits, has developed into an established, ISAPP-managed class of molecules, different from probiotics, prebiotics, and isolated metabolites. This review unites these two areas of research with a third, previously underappreciated line of research: the biochemistry of local fermented herbal medicines. Examples of the former include the Ayurvedic tradition of Asava-Arishta remedies and various regionally fermented drinks, such as Raabadi and Kanji, which have been used clinically for many centuries but never explored from a retinal perspective. From existing literature on the link between gut dysbiosis and diabetes and DR, the pathophysiology of NPDR driven by the oxidative stress and AGEs-RAGE interaction, and the categories of active metabolites formed through fermentation of medicinal plants, we appraise the literature and the mixed trial results concerning probiotics and synbiotics in glucose management, leading to the conclusion that a standardized postbiotic approach is more favorable compared to living organism methods. Based on all of the above, we develop an explicit five-step hypothesis about how postbiotic metabolites may help maintaining gut-retina axis homeostasis in early DR, and we propose a sequential in vitro, animal and human study to test the hypothesis. The article does not provide any new experimental data but provides an approach based on the literatures in different but mature fields that were not yet connected.
Reconceptualising Psychological Well-Being as A Catalyst for Health Workforce De-velopment, Organisational Resilience, And Sustainable Health Systems: A Theory-Building Bibliometric Analysis
Purpose: The study views Psychological Well-being (PWB) as the capability of an organization that can be built into a system beyond the mental health of the individuals. As a result, PWB would act as a driver/motivator for the development of health work and services, as well as the development of organizational capability, flexibility, and the establishment of health systems that can be sustained over time. Most bibliometric studies are content-driven and analyze the trends in publications and the thematic clusters of published studies. Contrary to that, the objective of this study is to construct the intellectual steps of PWB studies from a theory perspective and to construct an integrated theory to place psychological well-being at the starting point of building post-capacity health care organizations. This study analyzes the development of a resilient workforce and flexible organization, as well as sustainable health systems, based on psychological well-being. Design/methodology/approach: The study advocates theory building through bibliometric studies. The target population of the bibliometric study included publications from the years 2020 to 2025. A total of 3,813 publications were sampled. Publications were obtained from Scopus sub-databases through a bibliometric study with a particular focus on psychological well-being and general health-related studies. Data and meta-data were processed with Microsoft Excel and VOSviewer. A literature study was conducted for the analysis of the growth of publications, key authors, key journals, key institutions, key countries, key sponsors, citation structures, and research collaboration networks. Co-authorship and keyword co-occurrence, bibliographic coupling, and co-citation analysis were also employed to study the emerging research domains. Findings: Between 2020 and 2025, there was a marked increase in research studies on the psychological component of well-being, likely due to increased awareness of the element’s significance within healthcare systems. The United States, China, the United Kingdom, India and Turkey were the most prominent countries, and the National Institutes of Health, the National Natural Science Foundation of China, and the National Institute on Aging were active research funding agencies. Network studies indicated that research communities were beginning to cover the concepts of mental health, resilience, burnout, and healthcare workers, as well as the quality of life, well-being, and sustainability in the workplace and organizations. The bibliographic coupling and co-citation analyses illustrated that the research was beginning to include a variety of concepts beyond clinical and psychological, including organizational behavior, workforce management, public health, and healthcare management and health policy. With the knowledge from the various domains, this study attempts to define psychological well-being as a mental state of resilience and strength that empowers a flexible and sustainable health system. Originality/value: This study adds to the understanding of psychological well-being by placing it as a strategic capability for healthcare systems, moving away from limited individual constructs. Integrative theory-building bibliometrics is rare. Typically, bibliometrics describe patterns of research and publications. This study creates an explanatory theory-building bibliometrics model for the interrelationship of psychological well-being, health workforce development, organizational resilience, and sustainable health systems. The model demonstrates the relationship of psychological well-being to resilient healthcare systems and sustainable systems in the context of the foundational capability. The study provides healthcare leaders and policymakers practical ways to incorporate employee well-being into workforce planning, organizational resilience, and health system sustainability. The findings build a robust theoretical basis for the future empirical study of the psychological well-being of employees in the healthcare system in the determination of sustainable and resilient system integration.
Mohd. Hanizam Mansor, Abd. Rahim Romle, Mohd. Fo’ad Sakdan
Analysis of Dhikr Therapy's Impact on Drug Recovery: A Study of Trainees' Psychospiritual Well-Being
Drug addiction in Malaysia is a long-standing social issue that has negative implications for society. Various recovery methods have been introduced, including the Islamic psychospiritual approach, which is increasingly being applied in private rehabilitation centres. One of the private recovery centres that practises this approach is Pondok Remaja Inabah (1) Malaysia (PRI 1 Malaysia), which uses dhikr therapy as the primary method for recovery treatment. Although dhikr therapy in PRI (1) Malaysia has been proven to have a positive effect on trainees, its implementation and effects on the psychospirituality of trainees, especially on the elements of desire, spirit, heart and mind, are still poorly studied in depth. Therefore, this study aims to identify the implementation of dhikr therapy at PRI (1) Malaysia and examine its impact on the psychospiritual well-being of trainees. This study employs a qualitative approach that includes a literature review, participatory observation, and interviews with five trainees. The data were analysed both inductively and deductively with the help of NVivo 15 software. The study's results indicated that dhikr therapy has an influence and a positive impact on the psychospiritual elements of the trainees, including enhancing their patience and noble character (desire), increasing diligence and enjoyment of worship (spirit), increasing divine awareness and tranquillity (heart), and contributing to mental stability and maturity of thought (intellect). This positive impact began to be felt within 40 days of consistently practicing dhikr therapy. The implications of this study show that the dhikr therapy of the Tarekat Qodiriyah Naqsyabandiyah (TQN) has the potential to be an alternative approach to drug rehabilitation. Therefore, dhikr practice should be made a permanent module in drug rehabilitation programs, and it is recommended that parties, such as AADK and private rehabilitation centres, strengthen treatment methods, including implementing dhikr therapy as part of a holistic psychospiritual intervention component.
Abu Bakar Ahmad Mansor, Khairiah Mohd Yassin, Azman Md Zain
Human Resource Development As A Strategic Capability For Human Health: A Theory-Building Bibliometric Analysis Of Health Workforce Development, Organi-sational Resilience, And Sustainable Healthcare Performance
Purpose: This study creates a theory-building lens for HRD as a strategic organizational capability that bolsters health workforce development as well as staff and system resilience alongside sustainable healthcare performance. In contrast to most prior bibliometric studies that examined publication trends, research productivity, and collaboration networks, this study attempts a preliminary integrated theoretical analysis of the evolution of HRD in healthcare. This study seeks to advance the HRD field by introducing an Integrated Human Resource Development Strategic Capability Framework that positions HRD as the primary organizational capability that not only builds workforce development and organizational resilience, but also undergirds the sustainability of the healthcare system. This study purposely avoids presenting bibliometric evidence as a stand-alone contribution. Instead, the study consolidates the intellectual framework of HRD research and integrates a strategic capability model that articulates the interrelationships between Human Resource Development, Health Workforce Development, Organisational Resilience, and Sustainable Healthcare Performance. Design/methodology/approach: A theorizing bibliometric method was used on publications available on the Scopus database for the years 2020 to 2025. The author’s bibliometric data were collected using a structured search of the database guided by specific HRD and healthcare-related terms. The data set was analyzed using MS Excel and VOSViewer to illustrate trends and productivity as well as HRD in terms of its leading journals, key authors, institutions, nations, and sponsors. This was followed by analyses of citation structures, keyword co-occurrence, patterns of collaboration, bibliographic coupling, and co-citation. In contrast to a sole focus on standard descriptive bibliometric indicators, the results of the analyses were integrated to produce a strategic capability framework that delineates the major lines of development of HRD in healthcare. Findings: Findings indicate that research on Human Resource Development (HRD) has been growing rapidly throughout the world. It shows that scholars are beginning to realize that considering workforce capabilities and their development are vital to the sustainability of the healthcare system. The field's advanced intellectual structures have begun to include themes related to health workforce capability, digital transformation, workplace learning, workforce resilience, leadership, employee well-being, healthcare innovation, and sustainable performance. Based on the results, the proposed model places HRD as a core strategic capability of health workforce development and is the key contributor to developing workplace resilience and sustainable performance in health care by enhancing the quality, resilience, and innovative health care services combined with ongoing learning within the organization. Originality/value: The study moves beyond traditional bibliometric mapping in the fields of Human Resource Development and healthcare management to establish a theory-building explanation on intellectual development. The proposed Integrated Human Resource Development Strategic Capability Framework offers a theory of HRD, Integrated Strategic Capability Framework, and provides a resilient healthcare workforce developed within the framework of sustainable healthcare systems and services. The framework is designed to address the evolving needs and interests in healthcare systems of researchers, healthcare system leaders, and healthcare service managers.
Ahmad Shukri Adnan, Abd. Rahim Romle, Ahmad Martadha Mohamed
Causal Foundation Models for Trustworthy Multi-Modal Clinical Decision Intelligence: Integrating Medical Imaging, Electronic Health Records and Explainable Reasoning
Modern clinical artificial intelligence (AI) systems overwhelmingly rely on correlational, single-modality pattern recognition, a design choice that undermines robustness, fairness and clinician trust when models are deployed outside their training distribution. This paper proposes a Causal Foundation Model (CFM) framework for multi-modal clinical decision intelligence that jointly reasons over medical imaging and structured electronic health record (EHR) data through an explicit causal structure-learning layer, coupled with post-hoc and structure-grounded explainability. Unlike purely associative deep learning pipelines, the proposed architecture (i) learns a shared multimodal representation through cross-modal attention fusion, (ii) recovers a candidate causal graph over clinical variables using constraint-based causal discovery and (iii) grounds every prediction in both feature-attribution and causal-path rationales that a clinician can audit. We instantiate and evaluate a proof-of-concept version of the framework on the open UCI Heart Disease dataset (303 patients, 13 clinical variables) as the EHR modality, coupled with a clearly labelled synthetic radiographic-marker channel used only to demonstrate the multimodal fusion mechanism. Causal discovery using the PC algorithm recovers a clinically plausible skeleton in which chest pain type, exercise-induced angina, thalassemia status, ST-depression (oldpeak) and number of major vessels (ca) emerge as the strongest neighbours of the diagnostic target, consistent with established cardiology literature. A causally-informed feature subset achieves 83.8% accuracy and 0.914 AUC-ROC under five-fold stratified cross-validation, marginally outperforming both the unimodal EHR baseline (82.2% accuracy, 0.909 AUC) and the naively fused multimodal model (83.5% accuracy, 0.907 AUC), while using fewer than half the input features — evidence that causal feature selection can match or exceed brute-force fusion at substantially lower complexity. SHAP-based attribution further shows that clinically established risk indicators dominate the model's decisions, strengthening the case for causal, explainable architectures as a template for trustworthy clinical decision support. We discuss the limitations of the present proof-of-concept, outline a roadmap toward credentialed large-scale multimodal validation using resources such as MIMIC-CXR and MIMIC-IV and argue that causal grounding, rather than scale alone, is the more defensible route to clinically deployable, regulator-facing AI.
Digital Twin-Driven Explainable Generative AI Framework for Personalized Disease Progression Prediction and Precision Healthcare
Chronic disease management increasingly demands prediction tools that go beyond a single point estimate of risk and instead offer a personalized, time-evolving and uncertainty-aware picture of how an individual patient's condition is likely to unfold. This paper proposes a Digital Twin-Driven Explainable Generative AI (DT-XGAI) framework for personalized disease progression prediction, in which each patient's baseline clinical state is used to instantiate a probabilistic "digital twin" — a generative model capable of simulating a distribution of plausible future disease trajectories rather than a single deterministic forecast — coupled with a dual explainability layer combining feature-attribution and model-agnostic permutation-based rationale. We instantiate and rigorously evaluate a proof-of-concept version of this framework on the open scikit-learn Diabetes Progression dataset (Efron et al., 2004): 442 patients described by ten baseline physiological and serum biomarker variables, with a genuine quantitative one-year disease-progression outcome. A Gaussian-Process-based generative digital twin core is compared against Linear Regression, Random Forest and Gradient Boosting baselines under five-fold cross-validation. The generative digital twin achieved the best overall cross-validated performance (R² = 0.486 ± 0.061, MAE = 43.88 ± 1.97), modestly exceeding the Linear Regression baseline (R² = 0.478) and clearly outperforming Random Forest (R² = 0.442) and Gradient Boosting (R² = 0.427), while additionally providing calibrated, patient-specific predictive uncertainty that none of the deterministic baselines can produce. On a held-out test set, the digital twin achieved R² = 0.496 and MAE = 41.15 and posterior-sample trajectory simulation for representative patients is shown to produce personalized 95% confidence bands whose width varies meaningfully across individuals. SHAP-based attribution on a Gradient Boosting surrogate and permutation-importance analysis on the generative model independently converge on body mass index (BMI) and a serum lipid biomarker (s5) as the two dominant drivers of predicted progression, consistent with the original clinical findings on this cohort. These results provide honest, reproducible, small-scale evidence that generative, uncertainty-quantifying digital twin models can match or exceed deterministic point-prediction baselines while offering substantially richer, more clinically actionable output. We discuss the current proof-of-concept's limitations, outline a roadmap toward multimodal, longitudinal, credentialed clinical validation and argue that uncertainty-aware, explainable digital twins — not single-number risk scores — represent a more defensible foundation for precision healthcare decision support.
Green Synthesis, Physicochemical Characterization and in Vivo Anti Diabetic Evaluation of Eugenia Jambolana Mediated Silver Nanoparticles
Objective: Current study is designed to synthesize silver nanoparticles (AgNPs) using the ethanolic seed extract of Eugenia Jambolana through an eco-friendly green synthesis. Characterize the synthesized AgNPs using physicochemical methods, and investigate there in vivo antidiabetic potential in alloxan induced diabetic wistar rats. Methods: The silver nanoparticles were successfully synthesized using ethanolic seed extract of Eugenia Jambolana by using green synthesis and characterized by particle size, zeta potential, SEM and XRD. Moreover acute oral toxicity was evaluated. In vivo antidiabetic activity was evaluated using an alloxan induced diabetic Wistar rat model. The assessment included monitoring changes in body weight, fasting blood glucose, triglycerides, total cholesterol, serum creatinine, and key indicators of oxidative stress markers such as reduced glutathione, superoxide dismutase, and. malondialdehyde. Result: Physicochemical characterization confirmed the successful synthesis of stable AgNPs with crystalline nature, nanoscale particle size and good surface morphology. Acute toxicity studies shown that no mortality at limit dose. Animal studies demonstrated that the oral administration of AgNPs for 21 days significantly reduced FBG levels, total cholesterol, triglycerides, and serum creatinine levels compared with the diabetic control group along with restoration of body weight and improvement in lipid profile, Furthermore, oxidative stress markers showed significant improvement indicating improved antioxidant status. The antidiabetic efficacy of synthesized AgNPs was comparable to that of standard drug metformin. Conclusion: The synthesized AgNPs demonstrated potent antihyperglycemic, antioxidant and renoprotective effects, suggesting its potential as an environmentally sustainable nanotherapeutic approach for diabetes management.
Federated Deep Learning for Privacy-Preserving Breast Cancer Diagnosis from Multimodal Medical Data
Breast cancer is one of the major causes of death among women population and an effective clinical intervention requires accurate diagnosis at the early stage of the disease. While recent progress in deep learning has been made on automated breast cancer analysis from mammographic images, centralized training still has some significant issues regarding patient privacy, institutional data sharing constraints, and the heterogeneity of medical data distributions. Current CNN and federated learning approaches also suffer from the inability to capture relationship between patients and similarity aware diagnostic patterns that leads to lower classification robustness and high communication. In the view of the gaps in existing work the Graph-Based Federated Breast Cancer Prediction Network (GFBC-Net) is proposed in this paper using the CBIS-DDSM mammography dataset. We design the proposed framework that combines the convolutional feature extraction and patient similarity modeling using a graph neural network under a federated learning setup. First, mammogram images are preprocessed, region of interest (ROI) is extracted, and the images are normalized, and deep features are generated using CNN. Next, patient similarity graphs are created based on cosine similarity and K-nearest neighbor algorithms, with patient cases as nodes in the graphs and feature-level relationships as weighted edges. The local graph models are trained in the different distributed clients of hospitals but secure federated aggregation updates the global model without transferring any raw medical images. The experimental results show that GFBC-Net was able to attain an accuracy of 94.27%, precision of 93.88%, recall of 93.21%, F1-score of 93.54%, and AUC of 96.12%, outperforming the conventional CNN, federated CNN and graph-based baseline models. The study shows that the graph-guided federated learning method can better assist in breast cancer classification, while ensuring classification reliability and institutional data security to protect privacy.
Intelligent Injury Risk Assessment through Retrospective Analysis of Multimodal Sports Data among Professional Gymnasts
Sports injuries still pose a significant problem in professional gymnastics, as a consequence of the combination of repetitive movement patterns, high physical demands, long hours of training and accumulated physiological stress. Knowing early in the process of development when injury is likely to occur is important for athlete safety, continuing training and performance, and making evidence-based decisions. Traditional injury evaluation techniques rely on visual inspection and single physiological measurement, and have only a partial retrospective perspective of injury factors. The comparative analytical framework of this study involves machine learning and deep learning methods, to explore injury risk patterns based on retrospective analysis of multimodal sports injury records. The Multimodal Sports Injury Prediction Dataset is used, which consists of physiological, workload, and athlete condition indicators from professional sports participants. Before developing the models, data preprocessing was done which involved data cleaning, data normalization, class balancing, and statistical exploration. To predict injury risk, four machine learning models (Logistic Regression, Random Forest, XGBoost, LightGBM) and three deep learning models (Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)) were tested. The experimental analysis showed that the LightGBM model had the highest accuracy of 95.16%, F1-score of 94.92% and ROC–AUC of 0.981 compared to other machine learning models. In deep learning techniques, CNN showed the highest predictive power with 95.84% of accuracy, 0.986 ROC–AUC and 95.55% F1-score. An explainability analysis identified workload index, heart rate variability, recovery time and respiratory factors as dominant contributors to injury. The results show that multimodal retrospective injury analysis and prediction based on data can be useful for athlete risk monitoring and decision making in professional gymnastics.
Business Sustainability and Competitive Advantage: The Role of Innovation, Governance, and Strategic Management
Business sustainability has become a strategic priority for organizations seeking long-term growth in an increasingly competitive and dynamic business environment. Beyond achieving short-term financial success, firms are expected to create economic value while addressing environmental responsibilities and social expectations. As organizations respond to growing economic, environmental, and social challenges, innovation, corporate governance, and strategic management are increasingly recognized as mutually reinforcing capabilities. Individually, these concepts have received considerable scholarly attention; however, limited research has investigated how their integration contributes to sustainable organizational performance and long-term competitive success. This research proposes an integrated conceptual framework that brings together business sustainability, innovation, corporate governance, and strategic management to explain their collective contribution to long-term organizational success. Drawing on established theories and contemporary research, the paper examines how innovation supports organizational adaptability and value creation, how effective governance enhances transparency, accountability, and stakeholder confidence, and how strategic management aligns organizational resources with long-term sustainability objectives. The study further discusses the interaction among these elements and explains how their integration strengthens organizational resilience, improves operational performance, and supports sustained competitive positioning in rapidly evolving markets. Organizations are better positioned to sustain competitive success when innovation is reinforced by responsible governance and integrated into a coherent strategic management framework. Rather than viewing these capabilities as independent organizational functions, the study highlights their interconnected role in building sustainable business models capable of responding to technological change, market uncertainty, and evolving stakeholder expectations. The framework developed in this paper provides a practical foundation for managers, policymakers, and scholars to better understand how integrated organizational capabilities can support sustainable growth and strengthen long-term organizational value.
Evaluation of Vitamin B12 for Diabetic Kidney Disease in Iraqi Patients
Objective: In the US and across the world, diabetic kidney disease (DKD) is the primary cause of both chronic kidney disease (CKD) and end-stage renal disease (ESRD), impacting over 40% of those with type 2 diabetes and 30% of those with type 1 diabetes. Diabetic kidney disease (DKD) is categorised as a macrovascular consequence of diabetes. Research indicates that vitamin B12 deficiency is associated with DKD, whereas elevated vitamin B12 levels may be detrimental for individuals with CKD due to impaired cyanide metabolism. Material and Method: In a controlled study involving 45 volunteers aged 40 to 75 years, participants were assigned to three groups: controls, diabetics not receiving B12 supplements, and diabetics receiving B12 supplements. Result: Significant associations were observed between group classification and fasting blood sugar (FBS), HbA1c, duration of type 2 diabetes mellitus (T2DM), vitamin B12 levels, and age. However, increasing B12 levels did not significantly influence kidney function. Conclusion: This outcome may be attributed to the absence of pronounced kidney dysfunction in most participants, the relatively short duration of diabetes, and the limited sample size. Larger studies are required to clarify the relationship between vitamin B12 status and acute kidney complications.
A Hybrid PSO–GA–SA Optimized CNN–KNN Framework for Automated Multiclass Lung Cancer Classification from CT Images
Lung cancer is still one of the biggest causes of cancer deaths globally and it is essential to diagnose it accurately and timely from computed tomography (CT) images to enhance patient survival. While convolutional neural networks have been found to have good potential in automated CT-based classification, the performance of the networks is very much dependent on hyperparameter tuning, and the conventional hyperparameter tuning methods, such as manual search, grid search, and random search are computationally expensive and inefficient in high-dimensional space. In this study, a hybrid Particle Swarm Optimization–Genetic Algorithm–Simulated Annealing (PSO–GA–SA) methodology has been proposed to optimize CNN hyperparameters and CNN–KNN was designed to perform the automated multiclass lung cancer classification. The comprehensive preprocessing of the CT images is conducted to enhance the discriminative feature representation, such as resizing, grayscale, Gaussian filtering, Otsu thresholding, Canny edge detection, Sobel edge enhancement and channel fusion. The hybrid is a combination of PSO's global exploration capabilities, GA's evolutionary refinement capabilities and SA's local fine-tuning capabilities to achieve a balance between exploration and exploitation in optimization processes. These optimized parameters were tested on CNN, MobileNetV2, DenseNet121, InceptionV3 and Hybrid CNN-KNN using Augmented IQ-OTHNCCD where accuracy, precision, recall, F1-score, confusion matrix and learning curve were used to assess their performance. The hybrid PSO–GA–SA optimized CNN–KNN model exhibited the best performance with 100% accuracy, 99% precision, 100% recall, and 98% F1-score, outperforming the individually optimized and stand-alone models, indicating that it has promising potential as a reliable clinical decision-support tool.
Association of Dynamic Balance, Hitting Stability, Vertical Jump, and Tennis Performance among Elite Tennis Players with Chronic Ankle Instability- A Correlation Study
Background: Chronic ankle instability (CAI) is a common consequence of recurrent lateral ankle sprains and is associated with impaired proprioception, neuromuscular dysfunction, altered biomechanics, and reduced athletic performance. Although previous studies have independently examined balance, jump performance, and functional deficits, evidence investigating their collective relationship with tennis-specific performance in elite tennis players remains limited. Objective: To determine the association of chronic ankle instability with dynamic balance, vertical jump performance, hitting stability, and tennis performance among elite tennis players. Methods: A multicentre observational study was conducted among 200 elite tennis players (142 men and 58 women) aged 18–26 years from five tennis academies in Vellore District, India. Participants with chronic ankle instability were identified using the Cumberland Ankle Instability Tool and the Foot and Ankle Disability Index (FADI). Vertical Jump Test, Dynamic Stability Test, Hitting Stability Test, and Wiebe Tennis Performance Test (forehand and backhand) were performed. Data were analysed using descriptive statistics and Spearman's rank correlation after confirming non-normal data distribution using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Results: The mean age of participants was 21 ± 2 years, and the mean FADI score was 68 ± 7. FADI demonstrated a strong positive correlation with vertical jump performance (ρ = 0.856, p < 0.001) and hitting stability (ρ = 0.870, p < 0.001), whereas a strong negative correlation was observed with dynamic stability (ρ = −0.896, p < 0.001). Forehand and backhand performance exhibited a very strong positive correlation (ρ = 0.988, p < 0.001). Conclusion: Chronic ankle instability is strongly associated with impaired dynamic balance, reduced vertical jump performance, and diminished tennis-specific performance among elite tennis players.
Enhancing Mental Health Literacy and Reducing Depression Stigma: A Study of Vignette-Based Blended Teaching among Undergraduates
Introduction: Depression, a leading cause of death, often begins in adolescence and severely impacts undergraduate students. It affects their academic performance, leads to absenteeism, social isolation, and poor academic standing, highlighting the urgent need for intervention. This study aimed to evaluate the effectiveness of Vignette-Based Blended Teaching (VBBT) in improving mental health literacy regarding depression and reducing stigma among undergraduates. Methods: A quasi-experimental pre-test-post-test control group design was employed, involving 180 undergraduate students (95 in the intervention group and 85 in the control group) selected through simple random sampling. Data were collected using three instruments: the Mental Health Literacy Questionnaire for Depression, the Self-Stigma of Seeking Help Scale, and the Perception of Stigmatization by Others for Seeking Help scale. The VBBT intervention was delivered in two phases: Phase 1 involved online interactive teaching, followed by Phase 2, which consisted of three weeks of conventional face-to-face teaching. Data were analyzed using SPSS version 26.0, with results reported through descriptive and inferential statistics. Results: Post-test results showed a 52% increase in mental health literacy scores, with a reduction in self-stigma by 10.5% and perception of stigmatization by 48.8%. The intervention significantly improved mental health literacy (t(179) = 25.36, p<0.001), reduced self-stigma (t(179) = -9.59, p<0.001), and lowered perceived stigmatization (t(179) = 31.97, p<0.001). Conclusion: VBBT effectively enhances mental health literacy and reduces stigma regarding depression. Further studies are recommended to evaluate the long-term retention of these outcomes over one-month, three-month, and one-year intervals.
Investigating the Effectiveness of Balance and Strengthening Exercise and Modification of Home Environment on Risk for Fall among Older Adults in Rural Community
Background: India is an aging society, the prevalent problems experienced by the older adults include sleep disorder, problems with eating or feeding, incontinence, confusion, depression, evidence of fall and skin break down. Falls are a leading cause of death in older adults. Falls lead to 20% to 30% of mild to severe injuries and major cause of 10% to 15% of all emergency department visits. Fall related injuries typically occur in the home. The main purpose of this study is to determining the effect of family focused nursing interventions on level of risk for fall among older adults at selected rural community, Chennai (India). Materials and Methods: A true experimental study, two group pre and post-test was conducted among 220 older adults with risk of fall. Systematic sampling method was used to select the samples and screening of an older adults using Fulmer SPICES tool, risk for fall was assessed using Screening Assessment for Fall Evaluation (SAFE) tool. The balance and strengthening exercise and modification of home environment was administered to the older adults living in the community setting. Results And Discussion: After administration of intervention, the overall means score for risk for fall 3.37 with the SD 1.21, whereas in control group, it was 7.35 with the SD 1.93 with t value 18.26, proved that there was a high statistical significant reduction of risk for fall in the experimental group (29.7%) than the control group(1.6%). Conclusion: Balance and strengthening exercise and modification of home environment help to reduce the risk for fall significantly among older adults and promote active aging in community settings.
Factors Associated with Food Hygiene Practices among Home-Based Food Operators in Malaysia: The Role of Demographic Characteristics, Food Safety Awareness, and Moral Responsibility
Home-based food production has become an important component of community food systems. However, inadequate hygiene and environmental sanitation practices may increase the risk of food contamination and foodborne diseases. This study investigates how demographic characteristics influence food hygiene practices, food safety awareness, and moral responsibility among home-based food operators in Malaysia. We defined understanding as the actual cognitive dominion of food hygiene and safety principles; awareness mirrors psychological awareness and attitude towards their importance, and moral value gives the ethical compass that translates into real-life compliance behavior. This was a questionnaire survey of 303 home-based food operators, who formed the study sample. Results show very considerable differences by gender and then by levels of awareness and environmental food hygiene and safety, and variation in age, ethnicity, and educational level across some specific dimensions. The study shed light on the need of FHT, continuous monitoring towards food sanitation and developing hygiene and ethics among home-based food handlers. The findings contribute to environmental health management and sustainable community food safety policies.
Development and Psychometric Properties of the SELFCARE Intervention Module: A New Approach for Dealing with Non-Suicidal Self-Injury (NSSI) Clients
Objectives: This study aims to evaluate the validity and reliability of the psychoeducational module titled Structured Emotional and Learning Framework for Cognitive Adjustment and Relieving Social Anxiety (SELFCARE). Material and Methods: The module comprises eight sessions, each lasting 120 minutes. Data were collected using three distinct questionnaires to determine both content validity and reliability. The first questionnaire assessed the overall content validity of the module, while the second focused on the validity of individual sessions and activities. The third questionnaire was designed to measure the internal consistency reliability of the module. A panel of ten certified experts in counselling, module development, Cognitive Behavioral Theory (CBT), adolescent mental health, and psychology evaluated the module's validity. Additionally, a sample of 30 homogenous Form Two students from a secondary school in Kuala Muda, Kedah, selected through purposive sampling, participated in the reliability testing. Expert review data were analyzed using percentage agreement, while the data were analyzed using Cronbach’s alpha coefficient. Results: Findings from the expert evaluation revealed that the SELFCARE Module demonstrated excellent content validity, with 94% agreement well above the 70% benchmark for both overall content and the validity of individual sessions and activities. The reliability analysis showed a high Cronbach’s alpha value of 0.97, far exceeding the minimum acceptable threshold of 0.60. In conclusion, the results indicate that the SELFCARE Module possesses strong validity and reliability, and is ready to be implemented by full-time school counsellors to reduce emotional dysregulation, social anxiety, and cognitive distortion among students engaging in self-injurious behaviours.
Resilience and Mental Health among Incarcerated Women: The Mediating Role of Self-Acceptance
Objective: Incarcerated women are a high-risk population for mental health disorders, but the psychological resources that stabilize their well-being have not yet been adequately explored in Southeast Asian correctional settings. This study explored the association between resilience and incarcerated females' mental health and assessed if self-acceptance mediates the relationship of these constructs. Material and Methods: Using a quantitative cross-sectional survey design grounded both in Wagnild and Young’s conceptualisation of resilience and the self-acceptance principle of Rational Emotive Behaviour Therapy. By using systematic probability sampling, data were collected from incarcerated women at Kajang Women's Prison, Malaysia, the largest female correctional facility in the country. Resilience was measured by using the Resilience Scale, self-acceptance with the Unconditional Self-Acceptance Questionnaire and mental health measured by the Depression, Anxiety and Stress Scale-21. Data were processed and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) method and bootstrapping. Result: The measurement model was found to be adequate in its reliability and validity (Cronbach's alpha =.727–.915; composite reliability =.732–.971; AVE=.714–.855). The structural model showed that resilience strongly predicted both self-acceptance (β = .126, p = .007) and mental health (β = .206, p < .001), thus proving H1 and H2. However, self-acceptance did not significantly predict mental health (β= .063, p= .142). And resilience's indirect effect on mental health through self-acceptance was (β= .008, p= .390) unsupportive for H3 and H4, respectively. Conclusion: These results show that resilience has a direct, not self-acceptance-mediated effect on mental health among incarcerated women, with implications for targeted resilience-specific interventions in correctional facilities.
Integrating Emotional Intelligence and Readiness to Change in Addiction Recovery: Evidence from the DARE2change Module in Malaysia
Background and Objective: Addiction relapse remains a persistent challenge in Malaysia, with high rates reported by the National Anti Drugs Agency (AADK)¹. Emotional Intelligence (EI) and readiness to change are psychosocial constructs that influence recovery outcomes, yet few studies have examined their integration in structured rehabilitation programs. Guided by the Transtheoretical Model (TTM)², Self Determination Theory (SDT)³, Social Learning Theory (SLT)⁴, and Motivational Interviewing (MI)⁵, this study aimed to evaluate changes in readiness to change following participation in the DARE2change psychoeducational module and to test EI as a predictor of motivational progression. This figure illustrates the conceptual framework and outcomes of the DARE2change psychoeducational module. The left panel highlights Emotional Intelligence (EI) domains—emotional awareness, self regulation, and empathy—while the right panel depicts Readiness to Change stages based on the Transtheoretical Model (TTM)². The central section presents the DARE2change program components: psychoeducation, counseling, and group support. The lower section summarizes outcomes, including reduced relapse and increased resilience, and policy recommendations for integrating EI into rehabilitation programs⁶ˉ⁸. Materials and Methods: A quasi experimental study was conducted among 70 residents (35 treatment, 35 control) of Therapeutic Community Program (TC) recruited from a Drug Rehabilitation Centre under the Prisons Department of Malaysia. Participants were aged 21 years and above. Readiness to change was measured using the University of Rhode Island Change Assessment (URICA) instruments (TSPRA_ KE/TSPASCA_KE)⁹‚¹º while Emotional Intelligence (EI) was assessed using the USMEQ i, validated in Malaysia¹¹ which evaluates domains such as emotional awareness, regulation, and commitment. Statistical analyses included paired t tests, Pearson correlations, regression models, and MANOVA. Results: Readiness to change improved significantly in the treatment group (Mpre = 3.0991, Mpost = 3.2773; t(69) = −2.790, p = 0.007). Correlation analysis revealed moderate stability (r = 0.467, p < .001). Regression analysis indicated that EI domains, particularly Emotional Awareness and Commitment, significantly predicted readiness improvements. Effect size analysis (Cohen’s d ≈ 0.33) suggested a small to moderate practical impact. Conclusion: The DARE2change module enhanced readiness to change, while EI emerged as a significant predictor of motivational progression. These findings reinforce TTM’s stage‑based framework² and support global evidence that emotional regulation interventions reduce relapse risk¹²,¹³. Practically, rehabilitation programs should prioritize EI and readiness training as short‑term targets, while embedding longer‑term strategies to reshape self‑concept. Policymakers are encouraged to integrate structured modules such as DARE2change into national rehabilitation frameworks, aligning with Malaysia’s recovery goals