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Integrated machine learning, molecular docking, and molecular dynamics simulations for in silico identification of GSK3β inhibitors for Alzheimer’s disease
Abstract Glycogen Synthase Kinase-3 Beta is a multifunctional serine/threonine kinase, involved in regulating multiple cellular processes. Its dysregulation plays a key role in progression of Alzheimer’s disease and no FDA-approved GSK3β inhibitors for AD therapy are available, due to challenges in isoform selectivity, safety and pharmacokinetic limitations. Here, an OECD guideline-compliant, two-stage machine learning-based virtual screening framework is developed for GSK3β inhibitors. A chemically diverse dataset from different databases were pre-processed and used for model development and validation. A comparative study confirmed the superiority of this two-stage approach over standard multiclass models, by yielding significantly higher balanced accuracy on the internal test set (0.86 against 0.74) and specificity. The best predictive models were deployed as an open-access web tool and were used for screening ASINEX Synergy Library. In the structure-based approach, molecular docking with a validated docking protocol was performed and the best molecules were subjected to molecular dynamics simulation, binding free energy and per-residue decomposition analysis. Principal component analysis of trajectories confirmed global stability and consistent binding modes. Cross-screening against the homologous GSK3α isoform and the structurally distinct Cyclin-dependent kinase 2 (CDK2) by molecular docking revealed distinct mechanistic interaction profiles. Rather than exhibiting strict single-target exclusivity, the top hits showed binding affinity profiles consistent with potential CMGC family Multi-Target Directed Ligands (MTDLs), warranting experimental kinome validation. This presumed polypharmacological profile is advantageous for Alzheimer’s therapeutics, positioning these compounds as robust candidates for simultaneously mitigating multiple kinase pathways that drive Tau hyperphosphorylation. Overall, this integrated ML model development, validation, screening, protein selection, molecular docking and molecular dynamics workflow provides a reproducible, interpretable, and high-confidence method for identification of GSK3β inhibitors for Alzheimer’s disease.
Intramuscular innervation of the human masseter muscle: whole-mount Sihler analysis and a field-of-view–based descriptive framework
Abstract Prior Sihler-based and landmark-referenced studies have described intramuscular innervation of the human masseter. However, bilateral series-based descriptions focused on within-field arborisation morphology and the distribution of terminal branching within a standardised field of view remain limited. We analysed 60 masseter muscles from 30 adult body donors using a modified Sihler whole-mount staining protocol. A standardised circular field of view was evaluated to (1) classify within-field branching morphology and (2) localise the region of highest terminal arborisation density (HTAD) within the field. Two recurring within-field branching patterns were classified according to predefined morphological criteria: Type I, characterised by a compact branching configuration, and Type II, characterised by a broader fan-like/tree-like branching configuration within the photographed field. Type I predominated ( n = 24/30; 80%), whereas Type II was less frequent ( n = 6/30; 20%). The within-field pattern was bilaterally concordant in all individuals. The HTAD region was described using an operational sector-based definition within the photographed field-of-view and may provide a descriptive reference for understanding visible terminal arborisation morphology. These findings support hypothesis generation and highlight the need for future landmark-referenced whole-muscle studies to relate within-field branching patterns to motor entry points, functional correlates and clinically validated applications.
Rock phosphate composted with farmyard manure and phosphate solubilizing bacteria boosts maize yield in alkaline calcareous soils
Determinants and spatial patterns of early neonatal mortality in Somalia: a national analysis of the 2020 demographic and health survey
Topology-decoupled end-to-end framework for brain tumor MRI detection: boundary-preserving feature flow and inter-channel correlation distillation
Mycobiont and photobiont DNA barcoding revealed polymetallic rocks to be a rich source of specific lichen diversity
Linezolid alleviates d-galactose-induced anxiogenic or depressive behaviors and memory impairment
Osmotic stress alters exopolysaccharide partitioning in Bacteroides fragilis
Adaptive Learning Platform for Dyslexic Students
Identifying reactivation zones in the kotrupi landslide through UAV, satellite image and slope stability analysis
Science capital is related to probabilistic reasoning
Abstract Recognizing trustworthy scientific information is crucial in the current information era, and having sufficient resources to interact with and make use of scientific information is more important than ever. Because information is often presented numerically, informed decision-making requires numeracy skills such as probabilistic reasoning – understanding randomness and probability. The present study examined whether different dimensions of science capital (engagement in science-related activities, self-efficacy, early encouragement to science, and science attitudes) are associated with probabilistic reasoning measured as performance in a randomness task and tasks measuring representativeness and equiprobability heuristics. The results showed that engagement in science-related activities was related to reduced use of the representativeness heuristic and higher accuracy on randomness when controlling for age, gender, and education level. Interestingly, the use of equiprobability heuristic increased with science-related activities but decreased with early encouragement to science. We suggest that in adults, an accessible and encouraging science environment may be positively related to probabilistic reasoning.
Differences in follicular fluid kynurenic acid and 9,10-DiHOME levels between controlled ovarian stimulation and modified natural ART cycles
Identification of genomic regions and candidate genes for tuber micronutrient accumulation in potato (Solanum tuberosum L.) through GBS-based GWAS
Sex-specific mitochondrial dysregulation and metformin response in Wilson disease
Abstract Wilson disease (WD) is an inherited disorder of copper metabolism characterized by hepatic copper accumulation, mitochondrial injury, and systemic metabolic dysfunction. Metformin is a widely used antihyperglycemic agent with known effects on mitochondrial metabolism and cellular redox state. We tested whether metformin modifies hepatic mitochondrial abnormalities in a mouse model of WD, stratified by sex. Adult male and female Atp7b −/− mice were treated with metformin in drinking water (500 mg/kg/day) or water alone for 2 weeks. Liver mitochondria were evaluated by transmission electron microscopy and quantitative morphometry, respiratory chain enzyme activities, reactive oxygen species production, hepatic and mitochondrial copper and iron content, and targeted glucocorticoid and bile acid profiling. At baseline, female Atp7b −/− mice exhibited greater mitochondrial ultrastructural injury, smaller mitochondrial size, and higher hepatic and mitochondrial copper and iron levels compared with males. Metformin exposure was higher in females and was associated with marked changes in mitochondrial morphology, including increased size and more regularized structural features, along with alterations in respiratory enzyme activities, reduced oxidative stress, and changes in glucocorticoid and bile acid profiles. In contrast, males showed more limited structural and biochemical changes following treatment. These findings identify sex as an important determinant of mitochondrial phenotype and response to metformin in WD and support further investigation of sex-informed therapeutic strategies.