Ramaiah University of Applied Sciences (RUAS) is a UGC approved Private University in India.It was created by an act in the State of Karnataka, India and was established in December 2013.The university is sponsored by Gokula Education Foundation (Medical) trust. The university was created by integrating M.S. Ramaiah College of Hotel Management (1993), M.S. Ramaiah College of Pharmacy (1992), M.S. Ramaiah Dental College (1991) and the M.S. Ramaiah Advanced Learning Centre (2012). The campuses of the university is located at Mathikere and Peenya, Bengaluru, India.S.S.S.
A comprehensive theoretical investigation was carried out on two antihypertensive drugs, Hydrochlorothiazide (HCTZ) and Hydroflumethiazide (HFTZ), using Density Functional Theory (DFT) with the B3LYP-D3(BJ)/6-311++ G(d,p) hybrid functional including dispersion corrections.Hirshfeld surface analysis and fingerprint plots quantified intra- and intermolecular interactions, while the electron localization function (ELF) revealed atomic shell structures and localized lone pairs, especially around electronegative centers. Molecular docking studies highlighted significant binding interactions of both drugs with the OSR1 kinase protein, with HFTZ exhibiting superior inhibitory potential. Molecular dynamics simulations further confirmed the structural stability and interaction profiles of both drugs within the active site. HCTZ demonstrated a consistent interaction with GLY520, while HFTZ exhibited adaptive binding to the flexible loop regions, especially interacting with LEU473 through its sulfonamide group. These findings provide deep insights into the structural, electronic, and binding characteristics of these antihypertensive agents, supporting their pharmacological relevance and guiding future drug design strategies. This comparative framework highlights how subtle structural variations, such as fluorine substitution in HFTZ, influence electronic delocalization and binding behaviour.
The advanced wavelet-based activation function for machine-learning applications in Fiber Bragg Grating (FBG) sensors presented in this manuscript has been developed to enhance peak-detection performance. The traditional activation function is replaced with a new activation function derived from an efficient wavelet formulation. The proposed activation function demonstrates improved response characteristics, enabling more accurate peak detection when applied to machine-learning models. A deep feedforward neural network is used to develop the machine-learning training model, which is effective for this application. The goal of the proposed technique is to reduce the dependency on the optical spectrum analyser (OSA) for measuring peak wavelengths, strain, and temperature. The wavelet-based activation function is constructed as a combination of the Gaussian wavelet function and ReLU. The developed activation function for machine-learning applications exhibits high accuracy. The proposed peak-detection technique is also verified experimentally, showing good accuracy. The mean-square error is as low as 0.0012 pm for FBG-1 and 0.0015 pm for FBG-2, with the peak-area entropy measured at approximately 1 for both FBGs.
Chronic Obstructive Pulmonary Disease (COPD) is a global health concern, primarily linked to cigarette smoking. The potential role of electronic cigarettes (e-cigarettes) in COPD development remains unclear. Despite growing popularity as a smoking alternative, evidence suggests e-cigarettes may have harmful respiratory effects. This systematic review and meta-analysis assess the relationship between e-cigarette use and odds of having COPD. A comprehensive search of Web of Science, Embase, and PubMed was conducted to identify observational studies that assessed the association between e-cigarette use and the risk of COPD, providing risk estimates (hazard ratios, risk ratios, or odds ratios) for current, former, and ever e-cigarette users. Random-effects meta-analysis was performed using R software (V 4.4), and heterogeneity was assessed with the I2 statistic. Sensitivity analyses were conducted to test the robustness of the findings. Publication bias was evaluated using Egger’s test and funnel plots. Seventeen studies (1087 records screened) were included. E-cigarette use was associated with significantly higher odds of COPD compared to non-use. The pooled odds ratios were 1.48 (95
Alzheimer’s disease (AD) remains one of the most challenging neurodegenerative disorders, with limited therapeutic options and high failure rates in clinical trials. This work developed a drug repurposing pipeline powered by a machine learning (ML) model to find possible glycogen synthase kinase-3 beta (GSK-3β) inhibitors, a crucial target in AD pathogenesis. We selected, pre-processed, and optimized a dataset of 4,087 experimentally verified GSK-3β inhibitors using dimensionality reduction and descriptor creation. The most excellent prediction performance was obtained by Random Forest (100 descriptors) out of six supervised ML algorithms that were studied (R2 = 0.8178, RMSE = 0.8118, MAE = 0.6084). Following the virtual screening of 1,616 Food and Drug Administration (FDA)-approved drugs using this refined model, many compounds with projected IC₅₀ < 500 nM were found. Docking experiments showed insightful interactions and high binding affinities with the active-site residues of GSK-3β. With the best docking score (–9.3 kcal/mol), stable molecular dynamics (Average RMSD values (1000 ns): protein, 2.23 ± 0.93 Å; protein–ligand complex, 1.40 ± 0.43 Å) and long-lasting contacts with crucial residues, dolutegravir stood out among the top choices. ADMET profiling validated good pharmacokinetics and safety characteristics; however, possible hepatotoxicity needs more research. A HOMO–LUMO gap of 3.07 eV was found by density functional theory (DFT) analysis, indicating robust electron transport characteristics and balanced reactivity that are favorable for protein–ligand interaction. Together, these findings show that dolutegravir is a potential repurposable option against AD and how integrative ML, docking, MD, ADMET, and quantum chemistry techniques may speed up the identification of new drugs.
The Titanium metal matrix hybrid composite was developed and its wear behaviour was investigated in this study. Zirconium Oxide (ZnO2) and Graphene (Gr) were used as reinforcement particles. The liquid metallurgical (stir casting) method was implemented in developing the composite. Taguchi’s analysis and ANOVA methods were used to design the experiments and to identify the most influencing parameters on wear rate. ANOVA analysis results indicates that the Normal load (43.64
