Search Results (10,320)

The apple rust fungus Gymnosporangium yamadae (G. yamadae) secretes effector proteins into host apple leaf cells to facilitate parasitism. Among these, the candidate effector GyHRb12 was found to localize to the nucleus upon transient expression in Nicotiana benthamiana leaf cells, although its functional role remained unclear. Subsequent investigations demonstrated that overexpression of GyHRb12 protein decreases plant cell resistance and attenuates the transcription of multiple antifungal-related genes. Using a yeast two-hybrid screen, MdRPS20, a component of the 30S ribosomal subunit, was identified as an interactor of GyHRb12. Proteomic analysis revealed that GyHRb12 modulates the expression of proteins involved in protein translation processes, which may be mediated by changes in ribosomal abundance. Notably, mutating the 14th amino acid in MdRPS20 disrupted its interaction with GyHRb12, underscoring the critical role of this residue in effector recognition and subsequent suppression of host immunity. Collectively, these findings demonstrate that G. yamadae employs a nuclear-localized effector to target a ribosomal subunit protein, thereby reprogramming host translation activity and suppressing host immunity. Full article

Generative AI systems increasingly mediate how students retrieve literature and generate citations, shifting methodological rigor toward the maintenance of an auditable evidence trail. This study audits the search stage of AI-assisted literature review work, focusing on retrieval performance and citation traceability rather than downstream screening or synthesis. Four widely accessible tools were compared across two retrieval postures, and Boolean queries were executed against Scopus and evaluated against a DOI-verified librarian baseline built from Scopus, Web of Science, and Google Scholar. Using a canonical prompt and a bounded top-k capture rule (k = 20), each bibliographic record was evaluated for DOI traceability, DOI resolution integrity, metadata accuracy, and run-to-run drift. Records were screened through staged title/abstract and full-text eligibility review, and the final set included 37 studies after quality appraisal was 37 studies. Across sixteen audit runs, natural-language prompting frequently produced under-target yields, recurrent integrity failures, and low overlap with the librarian benchmark. Boolean translation improved run completion and increased the proportion of auditable records, but reproducibility remained unstable across repeated runs. These findings show that correctness at the record level does not ensure stability at the evidence-set level. Limitations include the bounded tool set, the search-stage focus, and the absence of downstream screening or synthesis evaluation. Retrieval posture, therefore, emerges as a practical governance lever for AI-assisted literature review workflows and supports the use of a student-facing verification checklist anchored in DOI verification and transparent protocol capture. This research received no external funding. OSF registration: Open Science Framework, 10.17605/OSF.IO/U8NHT. The manuscript reports the final included set as n = 37, states no external funding, and lists the OSF registration DOI. Full article

The escalating antimicrobial resistance (AMR) crisis necessitates the development of innovative anti-infectives with novel mechanisms of action. Nevertheless, research on natural products remains constrained by low-throughput screening and limited mechanistic insights. Artificial intelligence (AI) is catalyzing a pivotal paradigm shift—from the mere isolation of active compounds to precisely deciphering their modes of action. This review highlights AI’s transformative role in bridging ethnopharmacological knowledge and modern pharmacology to decode the mechanisms of plant-derived anti-infectives. Case studies on berberine, baicalein, danshensu derivatives, and rosmarinic acid derivatives from Coleus amboinicus illustrate AI’s capacity to map traditional therapeutic concepts to specific pathways (e.g., biofilm inhibition, inflammasome modulation) and to predict precise binding interactions and pharmacophores with high precision. Leveraging statistical correlations between ethnobotanical usage patterns and chemical similarity, we propose a “Knowledge–Data–Mechanism” three-layer framework centered on deep mechanistic insight. Integrating Chinese initiatives, such as the CNDR (China’s National Drug Repository) database and the TCM-AI platform, with global traditional medicine wisdom, this strategy provides an actionable roadmap for modernizing anti-infective discovery. Validated applications of this paradigm have demonstrated order-of-magnitude acceleration in mechanistic characterization, rapidly yielding structurally novel agents with well-defined, target-specific actions—a critical advancement in addressing the urgent global threat of antimicrobial resistance. Full article

Electrochemical sensors are user-friendly devices designed for the rapid and straightforward detection of target analytes. Serotonin (5-hydroxytryptamine, 5-HT) is a key neurotransmitter and neuromodulator that regulates diverse neuronal processes. Using a custom-designed screen-printed carbon electrode (SPCE) incorporating ordered mesoporous carbon–bimetal oxides of Cu and Ni (CuO–NiO–OMC), rapid and real-time detection of 5-HT was achieved. The CuO–NiO–OMC structure featured highly active CuO and NiO catalytic sites that effectively promoted the irreversible oxidation of 5-HT (vs. Ag/AgCl reference electrode). The CuO–NiO–OMC/SPCE sensor, connected to a portable potentiostat, exhibited exceptional electrocatalytic performance for the oxidation of 5-HT, with a detection limit of 42.5 nM. The sensitivity was 1.56 A M⁻¹ cm⁻², and the linear dynamic range was 0.0–80.0 µM. The CuO–NiO–OMC/SPCE sensor also demonstrated outstanding selectivity in the presence of competing neurochemicals, including norepinephrine, epinephrine, dopamine, and glutamate, as well as high concentrations of tested biomolecules and inorganic ions. Furthermore, the practicality of the sensor was demonstrated using human serum and urine samples, with recovery percentages ranging from 91.1% to 98.3%. Thus, the CuO–NiO–OMC/SPCE sensor offers an effective approach for 5-HT sensing, thereby permitting molecular-level understanding of brain function. Full article

Millions of new cancer cases and deaths worldwide annually demonstrate the pressing need for predictive preclinical models that go beyond standard two-dimensional (2D) cultures and animal systems. Recent developments in three-dimensional (3D) organoid technology have yielded a powerful platform for generating patient-specific mini-organ models that faithfully recapitulate primary tumors at the genetic, phenotypic, and architectural levels. Organoids retain functional fidelity by preserving key stem cell signaling pathways, including Wnt, Notch, and Hippo, making them robust platforms for disease modeling and high-throughput drug screening. This review describes representative organoid systems, ranging from patient-derived organoids (PDOs) to induced pluripotent stem cell (iPSC)-derived organoids, that serve as disease-specific “avatars” for personalized therapeutics. Predictive accuracy rates greater than 90% have been shown in clinical studies, providing evidence for the relevance of organoids in functional precision medicine. In addition to drug discovery, the extended use of organoids in regenerative oncology can provide a unique regulatory mechanism by selectively targeting CSCs and enhancing tissue repair after cytotoxic treatments. Recent advances in organoid-on-a-chip platforms, 3D bioprinting, and artificial intelligence (AI) address critical challenges involving vascularization, immune system integration, and scalability. With the advent of standardized, GMP-compliant platforms and recent regulatory initiatives, such as the FDA Modernization Act 2.0, organoids are well-positioned to support next-generation cancer research and therapy. This review aims to bridge the gap between stem cell-derived organoids (SCDOs), providing a fully humanized platform for preclinical cancer modeling and their clinical application, and to discuss their potential to advance ethically guided, personalized cancer therapeutics with improved predictive and translational power. Full article

Most camera view planning algorithms are employed in exploration tasks that maximise information gain, but few address the specific challenge of observing targeted surface areas with optimal image quality. This paper presents a novel camera view planning algorithm designed for dermoscopic mole mapping, which is crucial for early melanoma detection. Traditional full-body scanners, though beneficial, suffer from fixed camera positions that can compromise image quality due to varying body contours and patient sizes. Our algorithm addresses this limitation by dynamically optimizing the camera position on a set of collaborative robot (cobot) arms to enhance image resolution, safety, and viewing angles during skin examinations. The proposed method formulates the problem as a non-linear least-squares optimisation that ensures no camera occlusion and a safe distance from the end effector encapsulating the camera to the patient while adjusting the pose of the camera based on the topography of the body. This approach not only maintains optimal imaging conditions by considering resolution and angle of incidence but also prioritises patient safety by preventing physical contact between the camera and the patient. Extensive testing demonstrates that our algorithm adapts effectively to different body shapes and sizes, ensuring high-resolution images across various patient demographics. Moreover, the integration of our camera view planning algorithm into an intelligent dermoscopy system has shown promising results in improving the efficiency and geometric quality of dermoscopic image acquisition, which could lead to more reliable and faster diagnoses. This technology holds significant potential to transform melanoma screening and diagnosis, providing a scalable, safer, and more precise approach to dermatological imaging. Full article

Background: Recurrent vulvovaginal candidiasis (RVVC) is a chronic inflammatory disease primarily caused by Candida albicans (C. albicans). Its pathogenesis remains incompletely understood, and clinical management is challenged by recurrence and drug resistance. Ferroptosis, an iron-dependent form of programmed cell death driven by lipid peroxidation, has been implicated in various infectious and inflammatory diseases. However, its role in RVVC remains unclear, with a particular lack of evidence from clinical samples and animal experiments. Objective: This study aimed to investigate the association between RVVC and ferroptosis. First, we analyzed high-throughput sequencing data from human RVVC samples in the Gene Expression Omnibus (GEO) database to identify the expression profile of ferroptosis-related genes. Second, using an established murine model of chronic vulvovaginal candidiasis (CVVC), we validated changes in ferroptosis-related markers in vaginal tissues in vivo. Furthermore, an in vitro model of C. albicans-infected bone marrow-derived macrophages (BMDMs) was employed to explore the underlying mechanisms. This study provides experimental evidence for elucidating the pathogenesis of RVVC and exploring novel therapeutic strategies. Methods: The RVVC-related gene expression dataset GSE278036 was obtained from the GEO database. Differentially expressed genes (DEGs) were screened using the DESeq2 algorithm and intersected with ferroptosis-related genes from the FerrDb database to identify key targets. A protein–protein interaction (PPI) network was constructed using the STRING database and Cytoscape software, and hub genes were identified via the Betweenness centrality algorithm. Functional and pathway analyses, including gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways, were performed. Immune infiltration analysis characterized the immune microenvironment in RVVC patients. A CVVC mouse model was established in vivo, and a C. albicans-BMDMs infection model was established in vitro. The ferroptosis inhibitor ferrostatin-1 (Fer-1) was administered to investigate the pathological function and regulatory mechanisms of ferroptosis in RVVC at the molecular, cellular, and tissue levels. Results: Differential analysis identified 3132 DEGs in RVVC, which intersected with ferroptosis-related genes to yield 194 key targets. Among them, 20 hub genes were identified, including ferroptosis regulators and inflammatory factors. Functional enrichment analysis confirmed that these shared targets regulate RVVC pathology through a “ferroptosis-inflammation-immunity” multi-pathway network. Immune infiltration analysis revealed a specific immune disorder in RVVC patients characterized by “activation of the pro-inflammatory innate immune axis and suppression of the adaptive immune axis,” which was closely associated with ferroptosis-related genes. In vivo and in vitro experiments confirmed that C. albicans infection induced ferroptosis in vaginal tissues and macrophages, as manifested by lipid ROS accumulation, Fe²⁺ overload, GSH depletion, downregulation of GPX4 and SLC7A11, upregulation of ACSL4, 4-HNE, and MDA, and mitochondrial structural damage. Macrophages were identified as key target cells for ferroptosis, and their ferroptosis led to impaired antifungal function. Fer-1 treatment significantly inhibited ferroptosis, reduced vaginal histopathological damage and inflammatory cell infiltration, decreased fungal burden, downregulated abnormally elevated inflammatory factors, and restored Th1/Th2 immune balance. Furthermore, Fer-1 preserved macrophage viability and enhanced their antifungal killing capacity. Conclusions: This study provides the first evidence linking RVVC to ferroptosis through a combination of clinical data analysis and experiments, suggesting that ferroptosis is involved in its pathological process. These findings offer a new perspective for elucidating RVVC pathogenesis and developing targeted therapeutic strategies. Full article

Structure-based strategies are widely used in tuberculosis drug discovery; however, their translational impact remains limited. This review examines how structure-based virtual screening (SBVS) is applied in practice to Mycobacterium tuberculosis targets and explores why docking-derived predictions frequently fail to translate into measurable biological activity. Rather than treating docking scores as quantitative predictors of potency, representative case studies are analyzed to demonstrate that SBVS is most effective when employed as a prioritization framework integrated with appropriate target preparation, physicochemical filtering, and early experimental validation. Across diverse targets, molecular dynamics simulations emerge as a critical discriminator, enabling the identification of binding instability and false-positive hits that persist after static docking. Tuberculosis-specific constraints—including cofactor-dependent catalysis, resistance-associated mutations, membrane-rich environments, and permeability barriers—are discussed as key factors decoupling in silico affinity from whole-cell efficacy. Collectively, these observations support a workflow-oriented view of computational drug discovery in tuberculosis, in which iterative integration of structural modeling and experimental validation is required for meaningful lead identification. Full article

Atrial fibrillation (AF) frequently coexists with chronic coronary syndrome (CCS), reflecting shared cardiovascular risk factors and structural remodeling pathways. Identifying CCS patients at increased likelihood of AF remains clinically relevant, particularly when arrhythmia is silent or paroxysmal. Background: We hypothesized that established clinical and angiographic risk scores used in CCS may capture cumulative cardiovascular burden and could therefore assist in AF risk stratification. The biomarker-based ABC-stroke score was incorporated as a biological reference framework reflecting myocardial stress and injury. Methods: This prospective, single-center proof-of-concept study included 131 consecutive patients undergoing invasive coronary angiography for suspected myocardial ischemia. Patients were classified according to rhythm status, irrespective of AF subtype. Coronary artery disease severity was quantified using the Gensini and SYNTAX (PCI and CABG) scores. Global cardiovascular risk was assessed using Framingham, ASCVD, SCORE2, and SCORE2-OP. Correlation analyses, ROC curves, and multivariable logistic regression were performed to evaluate associations between risk scores, coronary complexity, and AF. Results: Clinical and angiographic risk scores differed significantly according to rhythm status and AF phenotype. Patients with AF exhibited higher global cardiovascular risk and greater coronary anatomical complexity compared with those in sinus rhythm. SYNTAX PCI and SYNTAX CABG demonstrated moderate discriminative performance for AF detection (AUC 0.745 and 0.760, respectively), with SYNTAX CABG remaining independently associated with AF in multivariable analysis. Significant correlations were observed between traditional cardiovascular risk scores and SYNTAX-derived measures of coronary complexity, whereas correlations with the Gensini score were weaker. The ABC-stroke reference model showed a strong discriminative signal, consistent with its biological proximity to AF-related myocardial stress. Conclusions: Established clinical and angiographic risk scores used in CCS are associated with the presence and phenotype of AF. These findings suggest that routinely available coronary risk assessment tools may serve as practical instruments for identifying CCS patients at increased likelihood of AF, potentially facilitating targeted rhythm screening and earlier risk stratification. Full article

UK ports handle the vast majority of national trade by volume and constitute Critical National Infrastructure. Since 2004, the SOLAS/ISPS Code and the Port Security Regulations 2009 have established baseline security requirements, recently supplemented by the National Security and Investment Act 2021 and the National Security Act 2023, creating overlapping obligations. This contribution maps the evolving regulatory framework (ISPS/Port Security Regulations, NSI 2021, NSA 2023, and CNI-related guidance). It assesses operational impacts using industry metrics and draws comparative lessons from Singapore and Rotterdam. Empirical research indicates that security regulation is not uniformly detrimental to performance: targeted, intelligence-led, and technology-enabled measures can coincide with productivity gains, whereas fragmented or blanket compliance regimes are more consistently associated with increased dwell times and throughput loss. These delays propagate through supply chains and intensify cost pressures, with proportionally greater impacts on mid-sized ports. Comparative evidence indicates that risk-based screening, integrated cyber–physical platforms, transparent governance, and clear cost-sharing frameworks can maintain security without compromising commercial performance. The contribution recommends (i) tiered, risk-based screening with transparent indicators; (ii) the consolidation of overlapping regulatory obligations; (iii) clearer liability frameworks, including model terms and alternative dispute resolution; and (iv) scheduled review provisions to maintain proportionality over time. Full article

Adsorption is a method widely used to remove low-molecular-weight organics from wastewaters, and phenolic compounds from olive mill wastewater are a persistent class of bioactive pollutants of environmental concern. We screened eleven microalgal candidates at 0.10 g·L⁻¹ using batch kinetics fitted with the Lagergren pseudo-first-order model to obtain rate constants (k) and fitted equilibrium capacities (q_e). Cyanobacteria, particularly Anabaena spp. and Limnospira platensis, exhibited the highest adsorptive potential in the screening; wall-less species (e.g., Dunaliella salina, Isochrysis galbana) showed negligible surface adsorption, indicating that the presence and type of cell wall highly influence biosorption. L. platensis was selected for detailed study because of its established industrial cultivation and valorisation potential. Equilibrium experiments with HCl-functionalized L. platensis at four biomass loadings (0.10–1.00 g·L⁻¹; initial phenolic mix 30 mg·L⁻¹) showed that increasing dose reduced equilibrium concentration (C_e) but decreased specific uptake from ≈77 mg·g⁻¹ to ≈18 mg·g⁻¹ while removal rose from ~26% to ~61%. Nonlinear isotherm fitting favoured the Freundlich model (1/n < 1), consistent with heterogeneous, multi-site adsorption. Targeted macromolecular extractions abolished phenol uptake, demonstrating that the intact protein–polysaccharide matrix is essential for binding. L. platensis route delivered higher single-cycle removal (≈61%) compared to the maize-derived activated carbon reference (≈49%) while also incurring a 1.3-fold lower GWP (approximately 3 kg CO₂-eq per treatment) than the activated carbon route (approximately 4 kg CO₂-eq per treatment) in our model. Overall, L. platensis represents a lower-impact alternative for natural phenols remediation, especially when integrated into valorisation pathways that recover algal co-products. Full article

Infertility is a prevalent clinical issue which disrupts normal human life and exerts an impact on fertility rates within the population. The increase in environmental pollutants, including acetyl tributyl citrate (ATBC), has given rise to concerns regarding their potential toxicity in infertility-related disorders. Icariin exhibits therapeutic effects on infertility, yet its mechanism of action against plasticiser-induced reproductive disorders remains unclear. This study aims to elucidate the potential toxicological targets and molecular mechanisms of ATBC-induced infertility, as well as the therapeutic targets and mechanisms of icariin in treating ATBC-induced reproductive disorders, through network toxicology, molecular-docking techniques and molecular dynamics simulation. Utilising the component-target database SwissTargetPrediction, the Similarity Ensemble Approach, PharmMapper, the ChEMBL database, and disease databases including the Therapeutic Target Database, OMIM, GeneCards, and DrugBank, 63 targets for ATBC-induced infertility and 33 targets for icariin treatment were identified. Screening via the STRING platform and Cytoscape 3.10.1 software yielded four core targets for ATBC-induced infertility—HSP90AA1, PIK3CA, CASP3, HRAS—and four core targets for icariin treatment—IL6, TNF, STAT3, and INS. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that ATBC-induced infertility correlates with pathways including pathways in cancer, prostate cancer, and PI3K-Akt signalling pathways. Conversely, the core targets of icariin therapy for related reproductive disorders are closely associated with tumour-associated signalling pathways and the AGE-RAGE signalling pathway. Molecular-docking and molecular dynamics simulation further confirmed the strong binding interactions between ATBC and infertility-related targets, as well as between icariin and core targets for treating reproductive disorders. This provides a theoretical foundation for understanding ATBC’s toxicological targets and the complex molecular mechanisms underpinning icariin’s treatment of infertility. It informs the development of strategies for icariin to prevent and treat infertility caused by exposure to ATBC-containing plastics or excessive ATBC contact. Full article

Background: Hepatitis B and C remain a major public health challenge in Greece, particularly amid demographic shifts, migration, and evolving socioeconomic conditions. Updated epidemiological data are essential to guide public health planning and prevention strategies. Methods: A repeated cross-sectional study was conducted among adults (n = 36,085) attending the General Hospital of Nikaia “Agios Panteleimon”, Piraeus, Greece, from 2018 to 2024. Participants consisted of inpatients and outpatients, including recognized high-risk groups. Serological markers assessed current hepatitis B infection (HBsAg) and past or recent hepatitis C exposure (anti-HCV). Associations were examined using univariate and multivariate logistic regression, reporting adjusted odds ratios (aORs) and 95% confidence intervals (CIs). Results: Overall prevalence was 4.65% for HBsAg (n = 1677) and 6.6% for anti-HCV (n = 2378). Females had significantly lower odds compared to males for both markers (HBsAg aOR = 0.24, anti-HCV aOR = 0.77, both p < 0.001). Anti-HCV prevalence declined with age, with the ≥70 group showing the lowest odds (aOR = 0.24, p < 0.001). For HBsAg, older age groups also showed reduced odds, particularly ages 60–69 (aOR = 0.49, p < 0.001) and ≥70 (aOR = 0.75, p = 0.005). Compared to Attica region, most regions had significantly lower odds of both infections, including Thrace (HBsAg aOR = 0.08; anti-HCV aOR = 0.32, both p < 0.001), Crete (HBsAg aOR = 0.13; anti-HCV aOR = 0.35, both p < 0.001), and Macedonia (HBsAg aOR = 0.37; anti-HCV aOR = 0.64, both p < 0.001). Compared to 2018, the odds were markedly higher in 2023 and peaked in 2024 for both infections (anti-HCV aOR = 1.78; HBsAg aOR = 3.10, both p < 0.001 for 2024). High-risk social groups demonstrated substantially elevated odds of anti-HCV (aORs 3.9–5.51, all p < 0.001), but had lower odds of HBsAg (aORs 0.32–0.60, all p ≤ 0.001). Conclusions: Increasing prevalence trends, regional disparities, and pronounced differences among vulnerable groups highlight the urgent need for strengthened screening, vaccination, and targeted hepatitis B and C prevention strategies, particularly among healthcare-attending and high-risk populations in Greece. Full article

This study evaluated the efficiency and ecotoxicological impact of the Fenton oxidation process with different metal-based catalysts (FeSO₄, CuSO₄, MnSO₄) in removing pharmaceuticals and organic contaminants from real hospital wastewater. All catalytic systems achieved high oxidation, with COD reduction reaching 81–89% after 4 h. Two complementary approaches were applied: targeted LC-MS/MS quantification of a model mixture of antibiotics and pharmaceuticals, and untargeted GC-MS/MS screening method for assessing the overall organic contaminant profile. Toxicity was assessed using Microtox^®. Targeted analysis showed complete or near-complete degradation of β-lactams, tetracyclines and most sulfonamides, with slightly lower removal for sulfamethoxazole in FeSO₄ system (96%). Fluoroquinolones and selected pharmaceuticals, such as caffeine and propranolol were more resistant, particularly with CuSO₄ and MnSO₄ catalysts. The untargeted GC-MS/MS screening revealed the highest overall reduction in chromatographic peak areas for FeSO₄ (70%), followed by MnSO₄ (39%) and CuSO₄ (36%). GC-MS/MS profiling confirmed that the Fe-catalyzed process was the most effective in reducing the total chromatographic peak area (70%). However, ecotoxicological assays revealed a significant increase in toxicity post-treatment, with growth inhibition of Allivibrio fischeri reaching 98%. This suggests that high oxidation does not directly correlate with biological safety, likely due to the presence of unconsumed reagents or the formation of transformation products with higher acute toxicity. These findings emphasize the necessity of integrating bioassays into treatment evaluation protocols to assess the true environmental risk of treated effluents. Full article

The present work set out to examine the antidiabetic capacity of Abelmoschus esculentus (okra) fruit extract through a combined experimental and computational framework. Enzyme inhibition assays were carried out against four metabolic targets, and IC₅₀ values stood at 7.66 ± 0.31 mg/mL for alpha-glucosidase, 5.21 ± 0.18 mg/mL for alpha-amylase, 2.11 ± 0.15 microg/mL for DPP-4, and 9.17 ± 0.54 mg/mL for pancreatic lipase. The extract showed moderate-to-weak activity relative to standard inhibitors acarbose, sitagliptin, and orlistat. Sixteen drug-like phytochemicals obtained from the IMPPAT 2.0 database were docked against the crystal structures of all four tested enzymes (PDB: 8CB1, 5E0F, 2ONC, 1LPB). Alpha-Carotene, Vitamin E, and Spiraeoside emerged as the top-ranked compounds across all targets, with alpha-Carotene recording the strongest binding affinity of −11.1 kcal/mol against pancreatic lipase, which was 4.2 kcal/mol more negative than the positive control orlistat (−6.9 kcal/mol). PLIP-based interaction profiling mapped out hydrogen bonds, hydrophobic contacts, pi-stacking, and salt bridges at the atomic level. Absorption, distribution, metabolism, and excretion (ADME) and toxicity screening of alpha-Carotene returned a favourable pharmacokinetic profile with predicted LD₅₀ of 1510 mg/kg (Class 4) and inactivity across most toxicity endpoints. A 100 ns molecular dynamics simulation of the pancreatic lipase-alpha–Carotene complex, alongside the orlistat control, showed stable root mean square deviation (RMSD) (0.15–0.22 nm), a consistent Rg (~1.97 nm), and sustained hydrogen bonding throughout the trajectory. Free-energy landscape analysis revealed a well-defined single energy basin for alpha-Carotene, suggesting a thermodynamically stable binding conformation. These findings lay the molecular basis for using okra phytochemicals as adjunctive agents in diabetes management, though in vivo validation remains necessary. Full article