Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images

Human thermography is considered to be an integral medical diagnostic tool for detecting heat patterns and measuring quantitative temperature data of the human body. It can be used in conjunction with other medical diagnostic procedures for getting comprehensive medication results. In the proposed study we have highlighted the significance of Infrared Thermography (IRT) and the role of machine learning in thermal medical image analysis for human health monitoring and various disease diagnosis in preliminary stages. The first part of the proposed study provides comprehensive information about the application of IRT in the diagnosis of various diseases such as skin and breast cancer detection in preliminary stages, dry eye syndromes, and ocular issues, liver disease, diabetes diagnosis and last but not least the novel COVID-19 virus. Whereas in the second phase we have proposed an autonomous breast tumor classification system using thermal breast images by employing state of the art Convolution Neural Network (CNN). The system achieves the overall accuracy of 80% and recall rate of 83.33%.

In-silico pharmacophoric and molecular docking-based drug discovery against the Main Protease (Mpro) of SARS-CoV-2, a causative agent COVID-19

COVID-19 (Coronavirus Disease 2019) caused by a novel 'SARS-CoV-2' virus resulted in public health emergencies across the world. An effective vaccine to cure this virus is not yet available, thus requires concerted efforts at various scales. In this study, we employed Computer-Aided Drug Design (CADD) based approach to identify the drug-like compounds - inhibiting the replication of the main protease (Mpro) of SARS-CoV-2. Our database search using an online tool "ZINC pharmer" retrieved ~1500 compounds based on pharmacophore features. Lipinski's rule was applied to further evaluate the drug-like compounds, followed by molecular docking-based screening, and the selection of screening ligand complex with Mprobased on S-score (higher than reference inhibitor) and root-mean-square deviation (RMSD) value (less than reference inhibitor) using AutoDock 4.2. Resultantly, ~200 compounds were identified having strong interaction with Mpro of SARS-CoV-2. After evaluating their binding energy using the AutoDock 4.2 software, three compounds (ZINC20291569, ZINC90403206, ZINC95480156) were identified that showed highest binding energy with Mproof SARS-CoV-2 and strong inhibition effect than the N3 (reference inhibitor). A good binding energy, drug likeness and effective pharmacokinetic parameters suggest that these candidates have greater potential to stop the replication of SARS-CoV-2, hence might lead to the cure of COVID-19.