Aim: Timely and accurate identification of crop leaf diseases is crucial for food security and sustainable agriculture. This study aims to develop a multi-crop leaf disease recognition framework that covers corn, potato, rice, and tomato, addressing the challenges associated with maintaining high performance under diverse agricultural imaging conditions. Methods: We utilized a balanced dataset of 21,000 curated leaf images spanning fourteen classes, including healthy and diseased samples. A custom convolutional neural network (CNN) was specifically designed to capture fine-grained lesion patterns. The model was trained using a unified pipeline with standardized data splits and preprocessing, ensuring reproducible evaluation. The performance was compared against standard deep learning architectures (ResNet-50, VGG-16) and traditional machine learning classifiers, including k-nearest neighbors (k-NN), support vector machine (SVM), and Random Forest. Results: The custom CNN achieved near-perfect classification accuracy on the test set, outperforming ResNet-50, VGG-16, and traditional machine learning classifiers under identical conditions. While the model showed potential for generalization to diverse field scenarios, the evaluation was limited to curated datasets. Additionally, the trained model was deployed in a web application to demonstrate real-time inference and practical utility for leaf disease assessment. Conclusions: This study establishes a robust and reproducible benchmark for multi-crop disease classification. The results highlight the model’s effectiveness in precision agriculture, supporting early intervention strategies, though further validation in field conditions is needed to confirm generalization.
Aim: This study aims to evaluate the antioxidant activities and profile the bioactive compounds of Sinularia sp. collected from the waters of Sabah, Malaysia. Methods: Total phenolic and flavonoid contents, as well as the antioxidant activity [2,2-diphenyl-1-picrylhydrazyl (DPPH); 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS)] of Sinularia sp. methanol extract were evaluated. Additionally, profiling of bioactive compounds was performed using Fourier transform infrared spectroscopy (FTIR), gas chromatography and mass spectrometry (GC-MS), and liquid chromatography-orbitrap-tandem mass spectrometry (LC-orbitrap-MS/MS) approaches. Results: Sinularia sp. methanol extract contained total phenolic content of 0.053 ± 0.001 mg gallic acid equivalent (GAE)/g and total flavonoid content of 1.78 ± 0.034 mg quercetin equivalents (QE)/g. The antioxidant activities determined by DPPH and ABTS assays showed half-maximal inhibitory concentration (IC50) values of 6.23 ± 0.17 mg/mL and 3.28 ± 0.07 mg/mL, respectively, while the standard ascorbic acid exhibited the highest activity with IC50 values of 0.15 ± 0.01 and 0.08 ± 0.01 mg/mL, respectively. FTIR analysis confirmed functional groups indicative of bioactive compounds, while GC-MS and LC-orbitrap-MS/MS identified 13 and 59 detections, respectively, containing fatty acids, fatty acid esters, alkaloids, steroids, and marine sterols. Interestingly, the key identified antioxidant compounds include vitamin E, docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA). Conclusions: In conclusion, Sinularia sp. exhibits promising antioxidant potential. Further studies are required to isolate and characterize active compounds for therapeutic applications.
Nutrition influences metabolic homeostasis through interactions among dietary patterns, gut microbial function, metabolite production, cellular stress, and immune regulation. However, the factors determining whether nutritional exposures support metabolic resilience or contribute to disease progression remain incompletely understood. This review synthesizes human and experimental evidence on how dietary patterns shape gut microbial function and metabolite production and how these microbial outputs relate to oxidative balance, proteostasis, and inflammatory signaling. It distinguishes microbial composition from functional activity, association from causation, and mechanistic plausibility from clinical relevance. Although current evidence supports functional roles for diet–microbiota interactions and microbial metabolites in metabolic regulation, reported effects vary across individuals and biological contexts. Methodological heterogeneity, limited longitudinal evidence, and incomplete translation of experimental findings constrain mechanistic interpretation in humans. Precision nutrition may help address this variability by integrating dietary assessment with microbiome function, metabolomics, metabolic phenotyping, and immune profiling. Its clinical value will depend on biomarkers and reproducible biological signatures that improve prediction and inform interventions beyond established dietary principles. Progress will require longitudinal studies, including controlled interventions, that link multi-omics profiles to meaningful metabolic and clinical outcomes.
Aim: Diet-induced oxidative stress and alterations in the intestinal microbial environment are central contributors to obesity-associated metabolic disorders. Functional foods rich in bioactive compounds have emerged as promising dietary interventions to mitigate these disturbances. Cyperus esculentus L. [tiger nut (TN)] is a nutrient-dense tuber rich in dietary fiber, oleic acid, tocopherols, flavonoids, phenolic acids, and phytosterols, which may exert antioxidant, hypolipidemic, and potential prebiotic effects. This study aims to investigate the dose-dependent effects of TN supplementation on oxidative stress, inflammatory status, lipid metabolism, liver histopathology, and selected culture-based gut bacterial indicators in a high-fat diet (HFD)-induced rat model. Methods: Twenty-five male rats were allocated into five groups: a normal control group, an HFD control group, and three HFD groups supplemented with TN powder at 10%, 20%, or 30% (w/w). Biochemical markers of oxidative stress, lipid profile, and inflammation were evaluated, and liver histopathology was examined. Gut bacterial assessment was performed using culture-based isolation and phenotypic identification of selected bacterial groups. Results: TN supplementation significantly increased anti-oxidant enzymes, reduced lipid peroxidation, improved serum lipid profile, and decreased inflammatory markers compared with the control group, with the greatest improvements observed in the 20% TN group. Histopathological examination demonstrated marked attenuation of HFD-induced hepatic injury, with the 20% TN group exhibiting near-normal hepatic architecture. Culture-based microbial assessment suggested that TN supplementation may favorably influence selected intestinal bacterial indicators. Conclusions: TN supplementation, particularly at the 20% inclusion level, effectively ameliorated HFD-induced oxidative stress, dyslipidemia, inflammation, and hepatic injury. Culture-based findings suggest that TN supplementation may favorably influence selected intestinal bacterial indicators. However, confirmation using sequencing-based microbiome approaches is required before definitive conclusions regarding prebiotic effects or gut microbiota modulation can be drawn.
Aim: This study aimed to identify novel plant-derived inhibitors targeting the guaB-encoded inosine monophosphate dehydrogenase (IMPDH, inosine 5’-monophosphate dehydrogenase) of the foodborne pathogen Campylobacter jejuni to address the rising global threat of multidrug-resistant infections. Method: The present research aimed to identify a novel candidate from medicinal plants using various drug discovery approaches, including network pharmacology, molecular docking, molecular dynamics (MD) simulations, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling to assess pharmacokinetic properties. Results: Molecular docking studies revealed that amaroswerin and eriocitrin had notable interactions with IMPDH, with docking scores of –8.612 and –8.488 kcal/mol, respectively. MD simulations for 200 ns showed that the amaroswerin-IMPDH complex remained stable until 99 ns, with a root mean square deviation (RMSD) of < 2.8 Å. However, this RMSD gradually increased and reached 4.5 Å by the end of the 200 ns trajectory. This indicates that complex underwent conformational adjustment. Amaroswerin showed a significant intestinal absorption rate of 49.29% and a notable permeability rate in Caco-2 cells. These data suggest it may be absorbed into the bloodstream more readily after oral administration. In terms of excretion, amaroswerin had a higher total clearance value of 0.465, indicating that it can be easily excreted from the body. ADMET analysis predicted moderate oral bioavailability and relatively favorable profiles across several toxicity endpoints, supporting amaroswerin as a promising candidate for further investigation. Nevertheless, these predictions are preliminary and require experimental validation of their pharmacokinetic and safety profiles. The organ toxicity and toxicity endpoints revealed that amaroswerin had higher inactive probability rates for hepatotoxicity (0.78), neurotoxicity (0.80), carcinogenicity (0.75), mutagenicity (0.66), and cytotoxicity (0.74), supporting its prioritization for further experimental evaluation. Conclusions: Based on the current computational findings, amaroswerin may be considered a promising lead compound for further investigation as an inhibitor of C. jejuni IMPDH. However, these findings do not establish antimicrobial efficacy or therapeutic suitability. Therefore, comprehensive in vitro and in vivo studies are required to experimentally validate IMPDH inhibition, antimicrobial activity, pharmacokinetic behavior, and safety before any consideration of clinical application.
