http://irgu.unigoa.ac.in/drs/handle/unigoa/5 Mon, 20 Apr 2026 13:36:37 GMT 2026-04-20T13:36:37Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7787 Exploring the interplay of electron density distribution and electrostatic potential in the interaction of nilutamide and flutamide with androgen receptors using quantum crystallography Balasubramanian, H.; Poomani, K.; Kandasamy, S.; Hathwar, V.R.; Gonnade, R.G. Prostate cancer is a malignant disease commonly found in men. Androgens support the growth and survival of prostate cancer cells. To control this growth and the spread of cancer cells, anti-androgen drugs are necessary to block androgen activity. Effective blocking of androgens depends mainly on the structure, intermolecular interactions and charge density distribution, electrostatic potential (ESP) and binding affinity of drug molecules. Nilutamide (NIL) and flutamide (FLU) are two structurally related non-steroidal anti-androgen drugs (NSAAs) which exhibit serious side effects. The present study explores the charge density distribution, electrostatic potential and intermolecular interactions of NIL and FLU determined from a high-resolution X-ray diffraction experiment and a solid-state quantum chemical theoretical study. Topological analysis of charge density reveals the electron density at the bond critical points of chemical bonds and intermolecular interactions. The electrostatic potential derived from the charge density distribution of both molecules in the crystal has been mapped, which allows a prediction of how the electrostatic interactions, hydrogen bonds, and van der Waals forces govern the binding of these two drug molecules with the androgen receptor at the electronic level. The ESP of interacting groups of both molecules in the androgen active site is approximated to the ESP of those groups in the crystals. The charge density distribution and the electrostatic potential of both molecules were compared. The difference in charge density is reflected in the ESP of NO sub(2), CF sub(3) and NH groups and the aromatic ring of both molecules, which is important for drug binding, metabolic stability and toxicity. A molecular docking simulation of both molecules with androgen receptors shows the difference in interactions and binding affinity in the binding pocket of the androgen receptor. The results of the high-resolution X-ray experiment and the advanced computational charge density study of NIL and FLU allows us to understand drug binding and is useful to relate their differing biological effects and toxicities at the electronic level. This information pertains to the design of a new potential androgen inhibitor with improved binding affinity and fewer side effects. Thu, 01 Jan 2026 00:00:00 GMT http://irgu.unigoa.ac.in/drs/handle/unigoa/7787 2026-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7777 Breaking the Zero Dimensional Illusion in Te(IV) Metal Halide Hybrids via Electronic Dimensionality Control Das, D.; Sarma, D.; Hathwar, V.R.; Mahata, A.; Kundu, J. Zero-dimensional (0D) metal halide hybrids (MHHs) containing ns sup(2) metal ions are attractive solid-state emitters, yet their photoluminescence quantum yields (PLQYs) often vary unpredictably even among structurally similar systems. To identify the factors governing emissivity, we investigate a series of 0D Te(IV)-based hybrids, A sub(2)TeCl sub(6) (A sup(+) = BzEt sub(3)N sup(+), BzMe sub(2)PhN sup(+), Ph sub(4)P sup(+), Ph sub(3)EtP sup(+)), which exhibit nearly identical optical features but display large differences in PLQY and lifetimes. Single-crystal X-ray diffraction, Hirshfeld surface analysis, and Voronoi polyhedral mapping reveal that these variations do not arise from local octahedral distortion but from subtle yet critical deviations in electronic dimensionality dictated by cation-dependent packing. We identify the interoctahedral halide-halide distance as a powerful structural descriptor correlating exciton delocalization and nonradiative quenching. This metric integrates both interoctahedral proximity and orientation effects, outperforming the conventional metric of metal-metal distances. DFT calculations elucidate excited-state relaxation pathways and reproduce the experimental emissivity trend. These results establish a clear structure-property relationship for 0D ns sub(2) metal halide hybrids, offering a predictive framework for designing high-performance luminescent materials. Thu, 01 Jan 2026 00:00:00 GMT http://irgu.unigoa.ac.in/drs/handle/unigoa/7777 2026-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7767 Detection of Avian Influenza-Infected Chickens using a Multi-Modal Audio-Visual Convolutional Neural Network Optimized for Indian Poultry Farms Gawas, M.; Patil, H.; Gawas, M.M. Avian influenza (AI) poses a recurring threat to poultry production and public health in India, where high-density farming and diverse environmental conditions create challenges for early detection. This study presents a novel Multi-Modal Chicken Health Monitoring System (MM-CHMS) that integrates audio-based spectral analysis of chicken vocalizations with visual-based posture and movement monitoring to detect AI infections. Our architecture combines Mel-spectrogram Convolutional Neural Networks (CNNs) for sound analysis with a ResNet-50 backbone for video feature extraction, fused through a fully connected feature integration layer. The system is designed for field deployment in Indian poultry farms, addressing constraints such as variable lighting, high background noise, and breed-specific vocal variations. A dataset was collected from farms in Goa, Karnataka, and Maharashtra, including both healthy and AI-infected flocks confirmed via RT-PCR tests. The proposed system supports both real-time edge-device inference and offline batch analysis. Field evaluations demonstrate an overall detection accuracy of 94.2 percent, with AI infections detected up to 48 hours prior to visible symptoms, enabling timely intervention. Wed, 01 Jan 2025 00:00:00 GMT http://irgu.unigoa.ac.in/drs/handle/unigoa/7767 2025-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7766 Integrating Thermal and Fecal Image Analysis Using Deep Learning for Early Detection of Poultry Diseases Gawas, M.; Gawas, M.M. Timely and accurate detection of poultry diseases is vital for minimizing economic losses and safeguarding animal welfare in the poultry industry. Conventional diagnostic methods are often invasive, time-consuming, and reliant on human observation, leading to potential delays in intervention. This study explores the application of artificial intelligence (AI) and non-invasive technologies to enhance disease diagnosis in poultry. A thermal imaging-based framework was developed to detect Avian Influenza (AI) and Newcastle Disease (ND) within 24 h of infection. Thermal data collected from both infected and healthy broilers were processed to extract 23 statistical features, which were then analyzed using Support Vector Machines (SVM) and Artificial Neural Networks (ANN). SVM outperformed ANN, achieving classification accuracies of 97.2 percent for AI and 100 percent for ND. To address uncertain predictions, the Dempster-Shafer evidence theory was employed to enhance decision reliability. Additionally, the study proposes a deep learning-based model using fecal image classification through CNN architectures such as MobileNetV2 and Xception to identify Coccidiosis, Salmonella, and ND, achieving accuracies exceeding 98 percent. The integration of thermal imaging, fecal image analysis, and AI-based classification presents a robust, non-invasive, and rapid disease detection system. This multi-modal approach demonstrates significant potential for improving poultry health monitoring and enabling early, automated interventions. Thu, 01 Jan 2026 00:00:00 GMT http://irgu.unigoa.ac.in/drs/handle/unigoa/7766 2026-01-01T00:00:00Z