Identification of potential probiotics from fermented sour traditional rice varieties and in vitro simulation studies.

Fermented products contain probiotic organisms that have beneficial and therapeutic effects on the gastrointestinal tract. The main objective of the study is to isolate probiotic bacteria from fermented sour traditional rice water and to evaluate their probiotic activity. The microbes were isolated from the fermented rice water and the characterization of the organisms was determined using MALDI-TOF MS. The morphological analysis, biochemical test, and carbohydrate fermentation test were done for further characterization. The colonization and therapeutic properties of organisms were evaluated by performing in vitro simulation studies. The results indicated that the isolated gram-positive organisms Pediococcus pentosaecus and Lactococcus lactis from traditional fermented sour rice water possessed desirable in vitro probiotic properties. Consuming fermented sour rice water enriches intestinal flora with beneficial bacteria and enzymes. Fermented rice water improves gut microbiome health, immune system function, and is also used to treat chronic conditions.

A Review on Deep Learning-driven Drug Discovery: Strategies, Tools and Applications.

It takes an average of 10-15 years to uncover and develop a new drug, and the process is incredibly time-consuming, expensive, difficult, and ineffective. In recent years the dramatic changes in the field of artificial intelligence (AI) have helped to overcome the challenges in the drug discovery pipeline. Artificial intelligence (AI) has taken root in various pharmaceutical sectors, from lead compound identification to clinical trials. Deep learning (DL) is a component of artificial intelligence (AI) that has excelled in many fields of Artificial intelligence (AI) research over the past decades. Its numerous applications in the realms of science and technology, especially in biomedicine and bioinformatics, are witnessed deep learning (DL) applications significantly accelerate drug discovery and pharmaceutical research in recent years, and their usefulness has exceeded expectations and shown good promise in tackling a range of issues with drug discovery. Deep learning (DL) holds great potential for drug development since it allows for sophisticated image interpretation, molecular structure and function prediction, and the automated creation of novel chemical entities with specific features. In the process of drug discovery, deep learning (DL) can be incorporated at all stages like identification of targets, prognostic biomarkers, drug designing and development, synergism and antagonism prediction, etc. This review summarizes various approaches of deep learning (DL) in drug discovery like deep generative models for drug discovery, deep learning (DL) tools for drug discovery, synergy prediction, and precision medicine.

Integrative transcriptome analysis of triple negative breast cancer profiles for identification of druggable targets.

As triple negative breast cancer (TNBC) lacks a specific target, exploration of abnormally expressed genes during the progression of TNBC is important for a better understanding of tumorigenesis and to find a specific target. We intended to figure out genes associated with TNBC, which can provide unique insights into gene dysregulation in TNBC while also pointing to new possible therapeutic targets for TNBC. A meta-analysis of multiple TNBC mRNA profiles was performed to identify consistently differentially expressed genes (CDGs). The pathways involved in modulating these genes were analyzed by MsigDB, and the interaction map was constructed. These CDGs were evaluated for their expression in cell lines, and drugs that could modulate the expression of CDGs were obtained using the connectivity map. CDGs were docked with doxorubicin and anethole, which is a phytocompound. The expression of selected CDGs was analyzed in MDA-MB-231 cells after treatment with doxorubicin and anethole. We found 45 CDGs, out of which 36 were upregulated and 9 were downregulated. MDA-MB-231 cell line was found to have high expression of CDGs, and drug that could modulate the expression of CDGs was doxorubicin. Docking results revealed that anethole and doxorubicin had good interaction with the CDGs especially with the genes AURKA, CDC6, DEPDC1, KIF23, KPNA2, MELK, CTNNB1, FLI1 and E2F1. Gene expression studies of the selected CDGs showed that the synergistic effect of anethole and doxorubicin effectively downregulated the expression. The CDGs identified from multiple cohorts have clinical significance and may be effectively exploited in the targeted therapy for TNBC.Communicated by Ramaswamy H. Sarma.