Thus, even if the material for toxicological tests is handled properly, detection of the presence of helium in a relatively short period of time after death is usually impossible or very difficult. If death due to inert gas inhalation is suspected during an autopsy, samples of biological material can be collected to be tested later by gas chromatography combined with mass spectrometry (GC-MS), but the results of the investigations are usually not helpful from the point of view of a forensic pathologist.Forensic Odontology deals with identifying humans based on their dental traits because of their robust nature. Classical methods of human identification require more manual effort and are difficult to use for large number of Images. A Novel way of automating the process of human identification by using deep learning approaches is proposed in this paper. Transfer learning using AlexNet is applied in three stages In the first stage, the features of the query tooth image are extracted and its location is identified as either in the upper or lower Jaw. In the second stage of transfer learning, the tooth is then classified into any of the four classes namely Molar, Premolar, Canine or Incisor. In the last stage, the classified tooth is then numbered according to the universal numbering system and finally the candidate identification is made by using distance as metrics. These three stage transfer learning approach proposed in this work helps in reducing the search space in the process of candidate matching. Also, instead of making the network classify all the 32 teeth into 32 different classes, this approach reduces the number of classes assigned to the classification layer in each stage thereby increasing the performance of the network. This work outperforms the classical approaches in terms of both accuracy and precision. The hit rate in human identification is also higher compared to the other state-of-art methods.On 31 December 2019, health authorities in the People’s Republic of China informed the World Health Organization of a then limited outbreak of interstitial viral pneumonia, identified at a laboratory in the city of Wuhan. In mid-April 2020 this outbreak of COVID-19 (as the disease has been called) has aggravated and spread worldwide, causing more than 200,000 deaths and affecting especially the United States, Spain, Italy, France and the United Kingdom. Despite the severity of the outbreak, the pathological findings have not been described in detail and there are very few guidelines or protocols for conducting autopsy studies on patients who have died from COVID-19. There are currently very few histopathological case series studies on this disease. In addition, some of these studies have been performed on biopsies or surgical resection pieces from patients in whom disease was subsequently demonstrated or through minimally invasive autopsy protocols. None of the studies offer a detailed necropsy protocol. This document proposes a protocol of action for the institutes of Forensic Medicine facing the current SARS-CoV2 pandemic, which combines protection of worker safety with optimization of tissue collection.The increasing concern on the harmful effects caused by mineral oil-based lubricants towards the environment has given impetus to the evolution of green-lubricants. Vegetable oils are highly biodegradable, renewable, and possesses good lubricating property. In the present study Pongamia pinnata, non-edible vegetable oil, also known as Karanja Oil (KO) was used as the base oil for a lubricant. The preliminary properties, such as fatty acid profile and viscosity, which has a vital role in governing the performance of lubricants were evaluated experimentally as per international standards. The shear viscosity of KO which constitutes 8 major fatty acids were predicted using non-equilibrium molecular dynamics (NEMD) and periodic perturbation (PP) method using Optimised Potentials for Liquid Simulations (OPLS) and Generalized Amber Force Field (GAFF). The shear viscosities were evaluated at temperatures ranging from 313K to 373 K and pressure P = 0.1 MPa. The experimental and simulation data of KO shear viscosity are in line with each other using OPLS. The kinematic viscosities were calculated using the shear viscosities and densities obtained from simulation. The variation between experimental and simulation data is less while using OPLS, while GAFF force fields resulted in higher deviations.Neutrophils synthesize four immune associated serine proteases Cathepsin G (CTSG), Elastase (ELANE), Proteinase 3 (PRTN3) and Neutrophil Serine Protease 4 (NSP4). While previously considered to be immune modulators, overexpression of neutrophil serine proteases correlates with various disease conditions. Therefore, identifying novel small molecules that can potentially control or inhibit the proteolytic activity of these proteases is crucial to revert or temper the aggravated disease phenotype. To the best of our knowledge, although there is limited data for inhibitors of other neutrophil protease members, there is no previous clinical study of a synthetic small molecule inhibitor targeting NSP4. In this study, an integrated molecular modeling algorithm was performed within a virtual drug repurposing study to identify novel inhibitors for NSP4, using clinically approved and investigation drugs library (∼8000 compounds). Based on our rigorous filtration, we found that following molecules Becatecarin, Iogulamide, Delprostenate and Iralukast are predicted to block the activity of NSP4 by interacting with core catalytic residues. learn more The selected ligands were energetically more favorable compared to the reference molecule. The result of this study identifies promising molecules as potential lead candidates.Growing concern about the difficulty in diagnosis and treatments of drug-resistant tuberculosis falls under the major global health issues. There is an urgent need for finding novel strategies to develop drugs or bioactive molecules against the global threat of Mycobacterium tuberculosis (MTB). Isoniazid (INH) is a front line drug against tuberculosis; it primarily targets the enoyl-acyl carrier protein reductase (InhA), a potent drug target in the mycolic acid pathway of MTB. To gain deeper insight into the impact of INH resistant mutation and its influence on the structural dynamics of InhA, combined conformational dynamics and residue interaction network (RIN) studies were performed. The molecular dynamics investigation provided a hint about the structural changes altering protein activity. The principal component analysis (PCA) based free energy landscape plot highlighted the highest stable part of wild-type (WT) and mutant structures. Intriguingly, the mutation at the 78th position of InhA from its native residue valine to alanine increases the structural stability with higher NADH binding affinity.