P. C. Jabin Science College is a science college in Hubballi, India run by the KLE Society, Belagavi. It is located in Vidyanagar, Hubballi, next to the KLE Technological University, Hubballi. The college is named after its principal donor Parappa Channappa Jabin of Hubballi. It is recognised under 2F and 12B of the UGC Act, June 1966.The college was reaccredited by NAAC and the UGC awarded the status of "College with Potential for excellence" in 2006.[citation needed] UGC has granted autonomous status to the college.
The detection of nitroaromatic compounds (NACs) is critically important due to their extensive use in explosive materials and the associated environmental and security risks. In this work, three structurally tailored coumarin derivatives 6-chloro-4-(4-methoxyphenoxymethyl)-chromen-2-one (S1), 1-(4-methoxyphenoxymethyl)-benzo[f]chromen-3-one (S2), and 6-methoxy-4-(4-methoxyphenoxymethyl)-chromen-2-one (S3) were explored as fluorescent probes for NAC sensing in Dimethyl sulfoxide (DMSO). Steady-state absorption, emission, and time-resolved spectroscopic investigations revealed significant fluorescence quenching upon gradual addition of nitrobenzene (NB), 2-nitrotoluene (2NT), 4-nitrotoluene (4NT), and 2,4,6-trinitrophenol (TNP). Stern–Volmer (S-V) analysis displayed positive deviations for NB, 2NT and 4NT, suggesting a combined dynamic and static quenching mechanism, whereas negative deviations observed for TNP indicates the major contribution from dynamic quenching mechanism. Fluorescence lifetime measurements confirmed dynamic quenching as the dominant pathway. Thermodynamic analysis revealed negative free energy change (ΔGPET) of the photoinduced electron transfer (PET) values for all coumarin–NAC systems, establishing the favourable nature of the PET process. Among the probes, S2 exhibited superior sensing performance owing to its extended π-conjugation and enhanced donor characteristics, resulting in the highest Stern–Volmer constants and quenching efficiencies. Both solution mode and contact mode studies demonstrated practical applicability, with S2 achieving the lowest detection limit for TNP (2.55 × 10⁻⁶ M). Overall, these findings highlight the potential of rationally designed coumarin derivatives as efficient, cost-effective, and selective fluorescent sensors for nitroaromatic detection, particularly for TNP.
Adverse Drug Reactions (ADRs), which often emerge in post-market surveillance or late-stage clinical trials, remain one of the biggest challenges in pharmaceutical safety. Challenges with existing computational models for ADR prediction involve inadequate protein-level representation and limited biological interpretability. Focusing on finding the optimal models, this research provides a comprehensive evaluation of different modeling strategies—deep learning, machine learning, and statistical methods for ADR pre- diction. We enhance protein-drug interaction (PDI) modeling by generating biologically interpretable protein embeddings through ProGen, a generative protein sequence model. A variety of deep learning models (Feed-Forward Neural Networks, Convolu- tional Neural Networks, Token Transformers, Graph Neural Networks), in addition to baseline machine learning (Random Forests, Support Vector Machines) and statistical approaches (Logistic Regression), utilize these embeddings along with drug molecular descriptors.Since ADR datasets in the real world are limited, experiments were conducted on a synthetic 1,000 drug-protein combi- nation dataset. The importance of representation learning in protein-drug interaction is emphasized through comparative analysis, which indicates that deep learning models, specifically Graph Neural Networks (F1-score: 94.77 % ) and Feed-Forward Neural Networks (Accuracy: 95.63 %), outperform their machine learning and statistical counterparts. By offering an interpretable and scalable model for the early detection of ADRs, this research contributes to safer drug development and reduces the incidence of clinical trial failure.
Reliable and efficient 3D protein structure prediction is at the forefront of contemporary bioinformatics and drug design. Though transformer architectures like AlphaFold2 are close to experimental accuracy, their computational expense and dependency on multiple sequence alignments (MSAs) restrict their application in large datasets. This work introduces a light-weight Graph Neural Network (GNN) architecture that represents proteins as residue-level graphs to predict 3D coordinates and per-residue confidence scores (pLDDT) at much reduced computation. The model attains RMSD < 2.5 Å, TM-score > 0.85, and excellent correlation with AlphaFold2 confidence scores (Pearson = 0.98, Spearman = 0.99). Comparative benchmarks show 30–100× faster inference and 70–80% less memory usage compared to AlphaFold2 at structural fidelity. Results indicate suitability for real-time proteomics, teaching, and resource-limited systems, making GNNs a promising, explainable, and scalable alternative for structural bioinformatics.
By employing solution technique, a modified PSSAMA crosslinked chitosan membranes were synthesized by incorporated copper nanoparticles to increase the hydrophilicity in membrane matrix. These membranes were characterized by FTIR, WAXD, TGA and SEM Studies. Membranes were tested for their ability to separate isopropyl alcohol / water at azeotropic point. The experiment results demonstrated that the membrane containing 1 wt% of nanoparticle showed highest separation selectivity of 56102 with a flux of 19.57×10⁻² kg/m² h for the azeotropic mixture at 30 ᵒC. The membranes were further tested for pervaporation at 40, 50 and 60 ᵒC for water containing feeds of aqueous isopropyl alcohol solution to confirm their stability at higher temperature. The total flux and the flux of water were found to be overlapping each other for all modified membranes, suggesting that these membranes could be used effectively to break the azeotropic point. From the temperature dependency of diffusion and permeation values, the Arrhenius activation parameters were estimated and discussed in terms of membrane efficiency. The CuO nanoparticles acted in increase in porosity that enhance hydrophilic property of the membrane. Total flux and flux of water were overlapping each other in membranes. The EP and ED values for water-iso propyl alcohol mixtures ranged between 10.42 to14.69 and 12.07 to16.70 kJ/mol.
