RESUMO
The structure and function of the cardiovascular system are modulated across the day by circadian rhythms, making this system susceptible to circadian rhythm disruption. Recent evidence demonstrated that short-term exposure to a pervasive circadian rhythm disruptor, artificial light at night (ALAN), increased inflammation and altered angiogenic transcripts in the hippocampi of mice. Here, we examined the effects of four nights of ALAN exposure on mouse hippocampal vascular networks. To do this, we analyzed 2D and 3D images of hippocampal vasculature and hippocampal transcriptomic profiles of mice exposed to ALAN. ALAN reduced vascular density in the CA1 and CA2/3 of female mice and the dentate gyrus of male mice. Network structure and connectivity were also impaired in the CA2/3 of female mice. These results demonstrate the rapid and potent effects of ALAN on cerebrovascular networks, highlighting the importance of ALAN mitigation in the context of health and cerebrovascular disease.
RESUMO
The automation of behavioral tracking and analysis in preclinical research can serve to advance the rate of research outcomes, increase experimental scalability, and challenge the scientific reproducibility crisis. Recent advances in the efficiency, accuracy, and accessibility of deep learning (DL) and machine learning (ML) frameworks are enabling this automation. As the ongoing opioid epidemic continues to worsen alongside increasing rates of chronic pain, there are ever-growing needs to understand opioid use disorders (OUDs) and identify non-opioid therapeutic options for pain. In this review, we examine how these related needs can be advanced by the development and validation of DL and ML resources for automated pain and withdrawal behavioral tracking. We aim to emphasize the utility of these tools for automated behavioral analysis, and we argue that currently developed models should be deployed to address novel questions in the fields of pain and OUD research.
