Tuberculosis globally affects millions of people every year and is responsible for high rates of mortality and morbidity in tropical countries like India. The treatment of tuberculosis involves using the first line of drugs especially Isoniazid, Pyrazinamide, Streptomycin, Ethambutol and Rifampicin for treatment under the DOTS (Directly Observed Treatment Shots) regime which can last up to minimum of six months. These drugs although widely used against Mycobacterium tuberculosis has given rise to multi drug resistant (MDR) tuberculosis strain. It has been observed widely that prolonged drug treatment for tuberculosis patient has rendered several side effects that include increasing muscle wasting and malnutrition. In our study, we have investigated the role of these major tuberculosis drugs namely Rifampicin, Streptomycin, Isoniazid, Pyrazinamide, and Ethambutol on actin polymerization which are famously known to be a central player in the sarcomere region of the muscle in human body. For in vitro studies, we have used biophysical approaches such as 90° scattering assay (RLS), size exclusion chromatography (SEC), Dynamic light scattering (DLS), Circular dichroism spectroscopy (CD), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), kinetic analysis to understand the time taken to break down effect of above mentioned drugs on actin disruption. In vivo analysis was carried out on yeast Δend3 mutants which are rich in F-actin filaments in order to understand the effect of the aforementioned drugs in rendering the muscle wasting phenomenon in tuberculosis. Furthermore, we also carried out in silico analysis to understand the probable modes of binding of these drugs on actin filaments. Communicated by Ramaswamy H. Sarma.Disorders due to substance use or addictive behaviours in the ICD-11 Abstract.  https://www.selleckchem.com/products/hc-7366.html  This paper concerns the revised classification of Substance-Related and Addictive Disorders in the 11th edition of the International Classification of Diseases (ICD-11) of the World Health Organization. The revision of the ICD serves to reflect changes in the understanding and diagnosis of addictive disorders and the need to improve clinical applicability. Regarding substance-related and non-substance-related addictive disorders, considerable innovations were introduced compared to the previous version. Major innovations include an expanded range of substance classes, significant adjustments (i. e., simplifications) in the conceptual and diagnostic guidelines of substance-related disorders, particularly "substance dependence", the introduction of the category of "addictive behaviour," and, associated with this, the assignment of "gambling disorder" to the addictive disorders, and the inclusion of the new (screen-related) "gaming disorder." In addition, for the first time the ICD catalogue includes an expansion of diagnostic options for early, preclinical phenotypes of addiction disorders ("Episodic Harmful Use"). This article summarizes the changes in the field of addiction disorders and discusses them from a child and adolescent psychiatric perspective.Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some of their counterparts usually using different performance metrics. Consequently, there lacks an accurate and complete picture of their merits and demerits, which makes it difficult for users to select proper algorithms for analyzing their data. To fill this gap, we first do a review on the major existing scRNA-seq data clustering methods, and then conduct a comprehensive performance comparison among them from multiple perspectives. We consider 13 state of the art scRNA-seq data clustering algorithms, and collect 12 publicly available real scRNA-seq datasets from the existing works to evaluate and compare these algorithms. Our comparative study shows that the existing methods are very diverse in performance. Even the top-performance algorithms do not perform well on all datasets, especially those with complex structures. This suggests that further research is required to explore more stable, accurate, and efficient clustering algorithms for scRNA-seq data.Cell survival requires the presence of essential proteins. Detection of essential proteins is relevant not only because of the critical biological functions they perform but also the role played by them as a drug target against pathogens. Several computational techniques are in place to identify essential proteins based on protein-protein interaction (PPI) network. Essential protein detection using only physical interaction data of proteins is challenging due to its inherent uncertainty. Hence, in this work, we propose a multiplex network-based framework that incorporates multiple protein interaction data from their physical, coexpression and phylogenetic profiles. An extended version termed as multiplex eigenvector centrality (MEC) is used to identify essential proteins from this network. The methodology integrates the score obtained from the multiplex analysis with subcellular localization and Gene Ontology information and is implemented using Saccharomyces cerevisiae datasets. The proposed method outperformed many recent essential protein prediction techniques in the literature.It is important to enhance penetration depth of nanomedicine and realise rapid drug release simultaneously at targeted tumour for improving anti-tumour efficiency of chemotherapeutic drugs. This project employed sodium alginate (Alg) as matrix material, to establish tumour-responsive nanogels with particle size conversion and drug controlled release functions. Specifically, tumour-targeting peptide CRGDK was conjugated with Alg first (CRGDK-Alg). Then, doxorubicin (DOX) was efficiently encapsulated in CRGDK-FeAlg nanogel during the cross-linking process (CRGDK-FeAlg/DOX). This system was closed during circulation. Once reaching tumour, the particle size of nanogels was reduced to ∼25 nm, which facilitated deep penetration of DOX in tumour tissues. After entering tumour cells, the size of nanogels was further reduced to ∼10 nm and DOX was released simultaneously. Meanwhile, FeAlg efficiently catalysed H2O2 to produce •OH by Fenton reaction, achieving local chemodynamic therapy without O2 mediation. Results showed CRGDK-FeAlg/DOX significantly inhibited tumour proliferation in vivo with V/V0 of 1.