A YOLO based software for automated detection and analysis of rodent behaviour in the open field arena.

Rodent models are important in mechanistic studies of the physiological and pathophysiological determinants of behaviour. The Open Field Test (OFT) is one of the most commonly utilised tests to assess rodent behaviour in a novel open environment. The key variables assessed in an OFT are general locomotor activity and exploratory behaviours and can be assessed manually or by automated systems. Although several automated systems exist, they are often expensive, difficult to use, or limited in the type of video that can be analysed. Here we describe a machine-learning algorithm - dubbed Cosevare - that uses a trained YOLOv3 DNN to identify and track movement of mice in the open-field arena. We validated Cosevare's capacity to accurately track locomotive and exploratory behaviour in 10 videos, comparing outputs generated by Cosevare with analysis by 5 manual scorers. Behavioural differences between control mice and those with diet-induced obesity (DIO) were also documented. We found the YOLOv3 based tracker to be accurate at identifying and tracking the mice within the open-field arena and in instances with variable backgrounds. Additionally, kinematic and spatial-based analysis demonstrated highly consistent scoring of locomotion, centre square duration (CSD) and entries (CSE) between Cosevare and manual scorers. Automated analysis was also able to distinguish behavioural differences between healthy control and DIO mice. The study found that a YOLOv3 based tracker is able to easily track mouse behaviour in the open field arena and supports machine learning as a potential future alternative for the assessment of animal behaviour in a wide range of species in differing environments and behavioural tests.

Efficacy of Probiotics in Patients of Cardiovascular Disease Risk: a Systematic Review and Meta-analysis.

This meta-analysis examined the effect of probiotics on outcomes associated with cardiovascular disease risk factors (high blood pressure, overweight BMI, high cholesterol and triglycerides, elevated HbA1c and serum glucose). All randomised controlled trials publish on PubMed, Scopus, Embase, Grey Literature and the Cochrane Central Register of Controlled Trials (CENTRAL) from 1990 to 2020 were systematically searched. The PEDro scale was used to assess the quality of studies. A total of 34 studies with 2177 adults were selected for inclusion in the analysis. The mean difference and effect size with a 95% confidence interval (CI) were analysed for the pooled results. Statistically significant pooled effects of probiotics were found in the reduction of systolic and diastolic blood pressure, total cholesterol, LDL-C, serum glucose, HbA1C and BMI; and elevation of HDL-C. No significant changes were observed in the outcome of triglycerides. Subgroup analysis revealed statistically significant effects of probiotics on the treatment of risk factors, with results favouring longer duration of treatment (> 1.5 months), use of alternate formulations (kefir and powder), higher dosage of probiotics (> 1.0 × 109 CFU), lower rate of study attrition (< 15%), double blinding of the study, diabetic patients and female populations. In summary, our meta-analysis showed a highly significant reduction in SBP, DBP associated with type 2 diabetes and in patients with diabetes mellitus, milk intake and more than 1.5 months duration intake. The effect on the reduction of total cholesterol LDL-C was associated with diabetes, hypertension, hypercholesterolemia, yoghurt intake and less than 1.5 months probiotic intake. The effect on the reduction of glucose and HbA1c was associated with diabetes, small dosage of probiotics, milk type and less than 1.5 months duration intake. Additionally, probiotic supplement had a beneficial effect in reducing BMI associated with obesity, higher dosage intake of probiotics and more than 1.5 months duration of intake.