Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

Background And Aims: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors.

Design: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data.

Identification of novel lncRNAs regulated by the TAL1 complex in T-cell acute lymphoblastic leukemia.

TAL1/SCL is one of the most prevalent oncogenes in T-cell acute lymphoblastic leukemia (T-ALL). TAL1 and its regulatory partners (GATA3, RUNX1, and MYB) positively regulate each other and coordinately regulate the expression of their downstream target genes in T-ALL cells. However, long non-coding RNAs (lncRNAs) regulated by these factors are largely unknown.