5-HT Receptors in the BNST Modulate Contextual Fear Conditioning Without Affecting Acute Early Life Stress-Enhanced Fear Learning in Adult Rats.

Background/objectives: Rodents provide a useful translational model of fear- and anxiety-related behaviors. Previously stressed animals exhibit physiological and behavioral stress responses that parallel those observed in anxious humans. Patients diagnosed with post-traumatic stress disorder (PTSD) present with a spectrum of debilitating anxiety symptoms that result from exposure to one or more traumatic events, with individuals exposed to early adverse experiences and women having increased vulnerability for diagnoses; however, the mechanisms of this increased vulnerability remain unknown.

IL6 and IL6R as Prognostic Biomarkers in Colorectal Cancer.

Colorectal cancer is the third most diagnosed malignancy worldwide and survival outcomes remain poor. Research is focused on the identification of novel prognostic and predictive biomarkers to improve clinical practice. There is robust evidence in the literature that inflammatory cytokine interleukin-6 (IL6) is elevated systemically in CRC patients and that this phenomenon is a predictor of poor survival outcome.

Combining Fundamental Kinetics and Standard Alkylation Assays to Prioritize Lead-Like KRAS G12C Inhibitors.

We measure the fundamental rate constants of internally discovered KRAS G12C inhibitors to demonstrate how kinetic analyses can be integrated with standard biochemical and cell-based assays for more optimal biophysical compound prioritization. In this proof-of-principle study, we characterize three irreversible covalent inhibitors targeting the mutant cysteine at the switch II binding pocket. We estimate the three fundamental kinetic rate constants ( , , ) that define the contributions of affinity and inactivation to the overall alkylation rate for a more complete biophysical characterization.

A Bayesian active learning platform for scalable combination drug screens.

Large-scale combination drug screens are generally considered intractable due to the immense number of possible combinations. Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute unobserved combinations. Here we propose BATCHIE, an orthogonal approach that conducts experiments dynamically in batches.