Shri Venkateshwara University (SVU) is a private university located in Gajraula, Uttar Pradesh, India. The university was established in 2010 through the Shri Venkateshwara University Uttar Pradesh Act, 2010 as a venture of the Venkateshwara Group Of Institutions. SVU offers courses in the fields of engineering, architecture, business studies, medical science, design and language and cultural studies, among others..
In this study, 15 genetically diverse wheat genotypes (10 lines and 5 testers) and their 50 F₁ hybrids were evaluated using a randomized design for 13 morphological and 2 biochemical traits under normal environmental conditions. Analysis of variance (ANOVA) revealed highly significant mean squares for both general combining ability (GCA) and specific combining ability (SCA) across all traits, indicating the involvement of both additive and non-additive gene actions. Among the parental genotypes, PBW-343, DBW-39, K-402, K-1317, KRL-210, and K-68 exhibited strong and consistent GCA effects, highlighting their potential as promising general combiners. For the hybrids, HD-3086 × HD-3171, K-402 × K-9107, K-1317 × K-9107, HD-2967 × K-0307, and K-402 × K-68 showed significantly positive SCA effects for grain yield per plant, suggesting a predominance of non-additive genetic variance in these crosses. In addition, high broad-sense heritability estimates were observed for plant height (77.32
Accurately predicting the unconfined compressive strength (UCS) of cement-stabilized soils is essential for designing safe and long-lasting geotechnical structures. Traditional empirical models often fall short in capturing the nonlinear interactions among geotechnical variables, cement content, and curing duration. This study introduces an explainable artificial intelligence (AI) framework for UCS prediction, utilizing seven advanced machine learning (ML) models: artificial neural network (ANN), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and CatBoost. A dataset of 500 samples with eight key input features—cement content, curing time, liquid limit, plasticity index, maximum dry density, optimum moisture content, fines content, and specific gravity—was used for model development. All models were evaluated using 10-fold cross-validation and multiple performance metrics, including R², RMSE, IOA, a20 accuracy, and prediction intervals. XGBoost achieved the best performance (R² = 0.923, RMSE = 0.269 MPa, IOA = 0.961, a20 = 94.8
[This corrects the article DOI: 10.7759/cureus.88238.].
Circadian rhythms exert fundamental control over gastrointestinal and metabolic physiology, governing 24-hour patterns of motility, secretion, nutrient absorption, microbial activity, immune regulation, and hepatic metabolism. Accumulating evidence indicates that the gastrointestinal tract is not an isolated system but is tightly integrated with systemic metabolic and neuroendocrine networks, forming a coordinated gastro-circadian metabolic axis (GCMA). This axis links molecular clocks in the gut, liver, adipose tissue, and skeletal muscle with rhythmic inputs from the gut microbiome, feeding-fasting cycles, autonomic signaling, and enteroendocrine mediators. Disruption of circadian alignment, through shift work, sleep deprivation, irregular meal timing, or nocturnal light exposure, leads to desynchronization between central and peripheral clocks, promoting inflammation, impaired epithelial barrier function, dysbiosis, altered bile acid signaling, insulin resistance, and disturbed energy homeostasis. These mechanisms contribute to a wide spectrum of gastrointestinal disorders, including gastroesophageal reflux disease, functional dyspepsia, irritable bowel syndrome, metabolic dysfunction-associated steatotic liver disease, inflammatory bowel disease, and potentially gastrointestinal malignancies. This review synthesizes molecular, translational, and clinical evidence to position the GCMA as a unifying framework for understanding circadian influences on digestive and metabolic disease. Importantly, it highlights emerging therapeutic opportunities in chronotherapy, including time-optimized pharmacotherapy, chrononutrition, and microbiota-targeted interventions. While current translation is limited by interindividual chronotype variability and heterogeneous clinical evidence, advances in wearable circadian monitoring, multi-omics profiling, and computational modeling offer promising avenues for precision implementation. Integrating GCMA principles into clinical practice may improve disease outcomes and establish circadian alignment as a cornerstone of preventive and therapeutic gastroenterology.
With the global rise in diabetes prevalence, the burden of diabetic kidney disease (DKD) is projected to escalate substantially, contributing to increased morbidity, mortality, and healthcare costs. This narrative review aims to synthesize contemporary evidence on guideline-directed medical therapy (GDMT) for DKD, with a specific focus on primary prevention, secondary prevention, and real-world implementation of evidence-based pharmacologic and non-pharmacologic strategies to mitigate renal and cardiovascular risk. A comprehensive literature review (published 2012-2025) was conducted, integrating international clinical guidelines, landmark randomized controlled trials, meta-analyses, and implementation studies addressing the prevention and management of DKD. Recent advances in GDMT have reshaped the therapeutic landscape of DKD. Lifestyle interventions - including dietary optimization, regular physical activity, and smoking cessation - demonstrate meaningful renoprotective and cardiometabolic benefits. Pharmacologic therapies, such as renin-angiotensin system (RAS) blockade, sodium-glucose co-transporter 2 (SGLT2) inhibitors, nonsteroidal mineralocorticoid receptor antagonists (MRAs), and glucagon-like peptide-1 receptor agonists (GLP-1 RAs), have shown robust efficacy in reducing albuminuria, slowing estimated glomerular filtration rate (eGFR) decline, and lowering cardiovascular events. Landmark trials report relative risk reductions of approximately 30-40% in kidney disease progression with SGLT2 inhibitors and 18-23% reductions in renal and cardiovascular composite outcomes with finerenone and GLP-1 RAs. Despite strong guideline endorsement, real-world uptake of GDMT remains suboptimal, particularly in low-resource healthcare settings. Early detection through routine eGFR and urine albumin-to-creatinine ratio screening, combined with timely initiation and sustained implementation of GDMT, offers the most effective strategy to alter the natural history of DKD. Integrating lifestyle modification with optimized pharmacotherapy is essential to reducing long-term renal and cardiovascular complications.
