Recurrent pregnancy loss (RPL) is a condition characterized by the loss of two or more pregnancies before 20 weeks of gestation. The causes of RPL are complex and can be due to a variety of factors, including genetic, immunological, hormonal, and environmental factors. This transcriptome data mining study was done to explore the differentially expressed genes (DEGs) and related pathways responsible for pathogenesis of RPL using an Insilco approach. RNAseq datasets from the Gene Expression Omnibus (GEO) database was used to extract RNAseq datasets of RPL. Meta-analysis was done by ExpressAnalyst. The functional and pathway enrichment analysis of DEGs were performed using KEGG and BINGO plugin of Cytoscape software. Protein-protein interaction was done using STRING and hub genes were identified. A total of 91 DEGs were identified, out of which 10 were downregulated and 81 were upregulated. Pathway analysis indicated that majority of DEGs were enriched in immunological pathways (IL-17 signalling pathway, TLR-signalling pathway, autoimmune thyroid disease), angiogenic VEGF-signalling pathway and cell-cycle signalling pathways. Of these, 10 hub genes with high connectivity were selected (CXCL8, CCND1, FOS, PTGS2, CTLA4, THBS1, MMP2, KDR, and CD80). Most of these genes are involved in maintenance of immune response at maternal-fetal interface. Further, in functional enrichment analyses revealed the highest node size in regulation of biological processes followed by cellular processes, their regulation and regulation of multicellular organismal process. This in-silico transcriptomics meta-analysis findings could potentially contribute in identifying novel biomarkers and therapeutic targets for RPL, which could lead to the development of new diagnostic and therapeutic strategies for this condition.

L. edodes (L. edodes) is the most consumed mushroom in the world and has been well known for its therapeutic potential as an edible and medicinal candidate, it contains dietary fibers, vitamins, proteins, minerals, and carbohydrates. In the current study butanolic extract of mushroom was used to form semisolid butanol extract. The current study aimed to explore biometabolites that might have biological activities in n-butanol extract of L. edodes using FT-IR and GC-MS and LC-MS. The synergistic properties of bioactive compounds were futher assessed by performing different biological assays such as antioxidant, anti-inflammatory and antidiabetic. FTIR spectra showed different functional groups including amide N-H group, Alkane (C-H stretching), and (C = C stretching) groups at different spectrum peaks in the range of 500 cm-1 to 5000 cm-1 respectively. GC-MS profiling of n-butanol extract depicted 34 potent biomolecules among those dimethyl; Morphine, 2TMS derivative; Benzoic acid, methyl ester 1-(2-methoxy-1-methylethoxy)-2-propanol were spotted at highest range. Results indicate that L. edodes n-butanol extract showed a maximum anti-inflammatory potential 91.4% at 300 mg/mL. Antioxidant activity was observed by measuring free radical scavenging activity which is 64.6% at optimized concentration along with good antidiabetic activity. In-silico study executed the biopotential of active ingredient morphine which proved the best docking score (- 7.0 kJ/mol) against aldose reductase. The in-silico drug design analysis was performed on biometabolites detected through GC-MS that might be a potential target for sulfatase-2 to treat ruminated arthritis. Morphine binds more strongly (- 7.9 kJ/mol) than other bioactive constituents indicated. QSAR and ADMET analysis shown that morphine is a good candidates against ruminated arthritis. The current study showed that L. edodes might be used as potent drug molecules to cure multiple ailments. As mushrooms have high bioactivity, they can be used against different diseases and to develop antibacterial drugs based on the current situation in the world in which drug resistance is going to increase due to misuse of antibiotics so new and noval biological active compounds are needed to overcome the situation.

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its \"End TB\" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Staphylococcus epidermidis is the most common cause of medical device-associated infections and is an opportunistic biofilm former. Among hospitalized patients, S. epidermidis infections are the most prevalent, and resistant to most antibiotics. In order to overcome this resistance, it is imperative to treat the infection at a cellular level. The present study aims to identify inhibitors of the prokaryotic cell division protein FtsZ a widely conserved component of bacterial cytokinesis. Two substrate binding sites are present on the FtsZ protein; the nucleotide-binding domain and the inter-domain binding sites. Molecular modeling was used to identify potential inhibitors against the binding sites of the FtsZ protein. One hundred thirty-eight chemical entities were virtually screened for the binding sites and revealed ten molecules, each with good binding affinities (docking score range -9.549 to -4.290 kcal/mol) compared to the reference control drug, i.e., Dacomitinib (-4.450 kcal/mol) and PC190723 (-4.694 kcal/mol) at nucleotide and inter-domain binding sites respectively. These top 10 hits were further analyzed for their ADMET properties and molecular dynamics simulations. The Chloro-derivative of GTP, naphthalene-1,3-diyl bis(3,4,5-trihydroxybenzoate), Guanosine triphosphate (GTP), morpholine and methylpiperazine derivative of GTP were identified as the lead molecules for nucleotide binding site whereas for inter-domain binding site, 1-(((amino(iminio)methyl)amino)methyl)-3-(3-(tert-butyl)phenyl)-6,7-dimethoxyisoquinolin-2-ium, and Chlorogenic acidwere identified as lead molecules. Molecular dynamics simulation and post MM/GBSA analysis of the complexes revealed good protein-ligand stability predicting them as potential inhibitors of FtsZ (Figure 1). Thus, identified FtsZ inhibitors are a promising lead compounds for S. epidermidis related infections.

Diabetes mellitus is a multifactorial disorder characterized by a chronic elevation in blood glucose levels. Currently, antidiabetic drugs are available to counteract the associated pathologies. Their concomitant effects necessitate the investigation for an effective and safe drug aimed to diminish blood glucose levels with fewer side effects. Several researchers are taking new initiatives to explore plant sources as they are known to contain a wide variety of active agents. Hence, the present study was undertaken to study the role of natural products using in silico interaction studies. Erythrin a compound present in lichens was selected as a potential anti-diabetic agent. Molecular docking studies were carried out with 14 target proteins to evaluate its antidiabetic potential. Molecular docking analysis resulted in favourable binding energy of interaction ranging as low as - 119.676 to - 92.9545 kcal/mol for erythrin, Analogue showed the highest interactions with 3C45 (- 119.676 kcal/mol) followed by 2Q5S (- 118.398 kcal/mol), 1XU7 (- 117.341 kcal/mol), 3K35 (- 114.267 kcal/mol). Erythrin was found to fare better than the three clinically used antidiabetic compounds, metformin, repaglinide and sitagliptin. Further, the molecular interactions between erythrin and the diabetes related target proteins was established by analysing the interactions with associated amino acids. In silico pharmacokinetics and toxicity profile of erythrin using admetSAR software predicted erythrin as non-carcinogenic and non-mutagenic. The drug-likeliness was calculated using molsoft software respecting Lipinski\'s rule of five. The compound was found to comply with Lipinksi rules violating only one filter criterion. The study suggested that erythrin could be a potential anti-diabetic agent.

Extracellular matrix is a dynamic meshwork of macromolecules that plays an important role in biological processes such as tissue remodeling and various developmental processes. Collagen is the chief component of ECM. Upon hydrolysis, it forms an irreversible left-handed helical structure which is further hydrolyzed by a specialized group of MMP family i.e. Gelatinases (MMP2 and MMP9). Present study was carried to figure out deleterious SNPs associated with MMP9 gene. Our results showed that two nsSNP (rs8125581 and rs41529445) that are present in catalytic domain are highly conserved and affect the protein structure and function.7 SNPs located in UTRs were found to alter miRNA seed region 13 SNPs of splice site were predicted to affect splice signals thereby affecting the post translational expression of MMP9. Most of the SNPs are still uncharacterized thereby present study provides a direction that can help to validate the relation between the altered expressions and functions of MMP9 protein in terms of disease susceptibility.

To investigate the role of Peroxidase and its physiological significance under Karnal Bunt (KB) were determined in resistant (HD-29) and susceptible genotype (WH-542) of wheat during different developmental stages. The enzymes were expressed constitutively in both the susceptible and resistant genotype. In gel assay and differential expression analysis of POD was significantly higher (p >0.05) in Sv and S2, than the S1 and S3 stages. in silico analysis of Peroxidase for eg. physico-chemical properties, secondary structural features and phylogenetic classification for comparative analysis. Motif and Domain analysis of Peroxidase by MEME, to be important for the biological functions, and studies of evolution. Our results clearly indicate that the enhanced expression of POD at the WS2 stage, which reinforces its role in stage dependent immunity against Karnal bunt and role of POD metabolism provides genotype and stage dependant structural barrier resistance in wheat against KB.