An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data.

Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.

Effect of melittin on iNOS and NF-κB expression induced by IL-1βin C518 cells.

Melittin (Mel), a natural detergent, is a major component of bee venom. Mel exhibits favorable clinical effects on the treatment of rheumatoid osteoarthritis, myositis, lumbar muscle strain, and peripheral neurological disorders. Interleukin-1β (IL-1β) contributes to the progression of osteoarthritis and is one of the key proinflammatory cytokines.

Promotive Role of CircATRNL1 on Chondrogenic Differentiation of BMSCs Mediated by miR-338-3p.

Aim: Bone marrow mesenchymal stem cells (BMSCs) are ideal seed cells for tissue engineering cartilage construction. However, the underlying mechanism of it has not been illuminate well. In this study, the effects of circATRNL1 (hsa_circ_0020093) on the differentiation of BMSCs into chondrocytes were investigated.