Finding and following: a deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo.

The last decade has seen increasing use of advanced imaging techniques in platelet research. However, there has been a lag in the development of image analysis methods, leaving much of the information trapped in images. Herein, we present a robust analytical pipeline for finding and following individual platelets over time in growing thrombi. Our pipeline covers four steps: detection, tracking, estimation of tracking accuracy, and quantification of platelet metrics. We detect platelets using a deep learning network for image segmentation, which we validated with proofreading by multiple experts. We then track platelets using a standard particle tracking algorithm and validate the tracks with custom image sampling - essential when following platelets within a dense thrombus. We show that our pipeline is more accurate than previously described methods. To demonstrate the utility of our analytical platform, we use it to show that in vivo thrombus formation is much faster than that ex vivo. Furthermore, platelets in vivo exhibit less passive movement in the direction of blood flow. Our tools are free and open source and written in the popular and user-friendly Python programming language. They empower researchers to accurately find and follow platelets in fluorescence microscopy experiments.

In this paper we describe computational tools to find and follow individual platelets in blood clots recorded with fluorescence microscopy. Our tools work in a diverse range of conditions, both in living animals and in artificial flow chamber models of thrombosis. Our work uses deep learning methods to achieve excellent accuracy. We also provide tools for visualizing data and estimating error rates, so you don't have to just trust the output. Our workflow measures platelet density, shape, and speed, which we use to demonstrate differences in the kinetics of clotting in living vessels versus a synthetic environment. The tools we wrote are open source, written in the popular Python programming language, and freely available to all. We hope they will be of use to other platelet researchers.

A review of the antimicrobial side of antidepressants and its putative implications on the gut microbiome.

OBJECTIVE: Serotonin reuptake inhibitors are the predominant treatment for major depressive disorder. In recent years, the diversity of the gut microbiota has emerged to play a significant role in the occurrence of major depressive disorder and other mood and anxiety disorders. Importantly, the role of the gut microbiota in the treatment of such disorders remains to be elucidated. Here, we provide a review of the literature regarding the effects of physiologically relevant concentrations of serotonin reuptake inhibitors on the gut microbiota and the implications this might have on their efficacy in the treatment of mood disorders. METHODS: First, an estimation of gut serotonin reuptake inhibitor concentrations was computed based on pharmacokinetic and gastrointestinal transit properties of serotonin reuptake inhibitors. Literature regarding the in vivo and in vitro antimicrobial properties of serotonin reuptake inhibitors was gathered, and the estimated gut concentrations were examined in the context of these data. Computer-based investigation revealed putative mechanisms for the antimicrobial effects of serotonin reuptake inhibitors. RESULTS: In vivo evidence using animal models shows an antimicrobial effect of serotonin reuptake inhibitors on the gut microbiota. Examination of the estimated physiological concentrations of serotonin reuptake inhibitors in the gastrointestinal tract collected from in vitro studies suggests that the microbial community of both the small intestine and the colon are exposed to serotonin reuptake inhibitors for at least 4 hours per day at concentrations that are likely to exert an antimicrobial effect. The potential mechanisms of the effect of serotonin reuptake inhibitors on the gut microbiota were postulated to include inhibition of efflux pumps and/or amino acid transporters. CONCLUSION: This review raises important issues regarding the role that gut microbiota play in the treatment of mood-related behaviours, which holds substantial potential clinical outcomes for patients suffering from major depressive disorder and other mood-related disorders.