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Cocaine abuse increases the risk of cardiovascular mortality and morbidity; however, the underlying molecular mechanisms remain elusive. By using a mouse model for cocaine abuse/use, we found that repeated cocaine injection led to increased blood pressure and aortic stiffness in mice associated with elevated levels of reactive oxygen species (ROS) in the aortas, a phenomenon similar to that observed in hypertensive humans. This ROS elevation was correlated with downregulation of Me1 (malic enzyme 1), an important redox molecule that counteracts ROS generation, and upregulation of microRNA (miR)-30c-5p that targets Me1 expression by directly binding to its 3'UTR (untranslated region). Remarkably, lentivirus-mediated overexpression of miR-30c-5p in aortic smooth muscle cells recapitulated the effect of cocaine on Me1 suppression, which in turn led to ROS elevation. Moreover, in vivo silencing of miR-30c-5p in smooth muscle cells resulted in Me1 upregulation, ROS reduction, and significantly suppressed cocaine-induced increases in blood pressure and aortic stiffness-a similar effect to that produced by treatment with the antioxidant N-acetyl cysteine. Discovery of this novel cocaine-↑miR-30c-5p-↓Me1-↑ROS pathway provides a potential new therapeutic avenue for treatment of cocaine abuse-related cardiovascular disease.

Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing algorithms on datasets used in prior work. In many cases, this data is used as a benchmark to demonstrate good performance without accounting for the complexities of criminal justice (CJ) processes. We show that pretrial RAI datasets contain numerous measurement biases and errors inherent to CJ pretrial evidence and due to disparities in discretion and deployment, are limited in making claims about real-world outcomes, making the datasets a poor fit for benchmarking under assumptions of ground truth and real-world impact. Conventional practices of simply replicating previous data experiments may implicitly inherit or edify normative positions without explicitly interrogating assumptions. With context of how interdisciplinary fields have engaged in CJ research, algorithmic fairness practices are misaligned for meaningful contribution in the context of CJ, and would benefit from transparent engagement with normative considerations and values related to fairness, justice, and equality. These factors prompt questions about whether benchmarks for intrinsically socio-technical systems like the CJ system can exist in a beneficial and ethical way.