Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.

Background And Objectives: The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts.

Redox status and metabolomic profiling of thioredoxin reductase inhibitors and 4 kGy ionizing radiation-exposed Deinococcus radiodurans.

The gram-positive bacterium Deinococcus radiodurans can survive under extreme ionizing radiation environment. This study aims to rationalize the role of redox balance, antioxidant status, and metabolite content on the radiation survival of D. radiodurans.

Evaluation of dissimilar intestinal bacteria incorporated feeds on growth of ornamental fish Swordtail Xiphophorus helleri.

Four dissimilar bacterial colonies were isolated from the intestine of ornamental fish Swordtail Xiphophorus helleri through serial dilution. The isolated colonies were identified as Enterobacter sp., Bacillus sp.

Phytochemicals reverse P-glycoprotein mediated multidrug resistance via signal transduction pathways.

P-glycoprotein, encoded by ATP-binding cassette transporters B1 gene (ABCB1), renders multidrug resistance (MDR) during cancer chemotherapy. Several synthetic small molecule inhibitors affect P-glycoprotein (P-gp) transport function in MDR tumor cells. However, inhibition of P-gp transport function adversely accumulates chemotherapeutic drugs in non-target normal tissues.

Ginsenoside Rg1 Induces Apoptotic Cell Death in Triple-Negative Breast Cancer Cell Lines and Prevents Carcinogen-Induced Breast Tumorigenesis in Sprague Dawley Rats.

The objective of this study is to investigate the anticancer potential of ginsenoside Rg1 using and experimental models. In this study, we found that ginsenoside Rg1 induces cytotoxicity and apoptotic cell death through reactive oxygen species (ROS) generation and alterations in mitochondrial membrane potential (MMP) in the triple-negative breast cancer cells (MDA-MB-MD-231 cell lines). We found that ginsenoside Rg1 induces the formation of gamma H2AX foci, an indication of DNA damage, and subsequent TUNEL positive apoptotic nuclei in the MDA-MB-MD-231 cell lines.