Comparison of statistical, machine learning, and mathematical modelling methods to investigate the effect of ageing on dog's cardiovascular system

The aim of this work is to provide a preliminary comparison of different classes of methods to automatically detect the effect of ageing from in vivo data. The application which motivated this work is related to safety pharmacology, whose major goal is to determine, in a pre-clinical phase, whether a drug is potentially dangerous for the health [1]. In particular, we are going to compare statistical, machine learning and mathematical modelling methods.L'objectif de ce travail est de fournir une comparaison préliminaire entre différents classes de méthodes pour la détection automatique de l'effet du viellissement sur le système cardiovasculaire, en exploitant des données in vivo. L'application qui a motivé ce travail est liée à la pharmacologie de sécurité, qui vise à établir, dans une phase pre-clinique, si un médicament est potentiellement dangereux pour la santé [1]. En particulier, nous allons comparer des approches statistiques, d'apprentissage statistique et de modélisation mathématique

Comparison of statistical, machine learning, and mathematical modelling methods to investigate the effect of ageing on dog’s cardiovascular system

The aim of this work is to provide a preliminary comparison of different classes of methods to automatically detect the effect of ageing from in vivo data. The application which motivated this work is related to safety pharmacology, whose major goal is to determine, in a pre-clinical phase, whether a drug is potentially dangerous for the health. In particular, we are going to compare statistical, machine learning and mathematical modelling methods

Comparison of statistical, machine learning, and mathematical modelling methods to investigate the effect of ageing on dog’s cardiovascular system

The aim of this work is to provide a preliminary comparison of different classes of methods to automatically detect the effect of ageing from in vivo data. The application which motivated this work is related to safety pharmacology, whose major goal is to determine, in a pre-clinical phase, whether a drug is potentially dangerous for the health. In particular, we are going to compare statistical, machine learning and mathematical modelling methods

An in silico approach to monitor and predict haemodynamics during safety pharmacology studies

International audienceThe present work is related to develop in silico models in order to predict the impact of drugs on the haemodynamics. More precisely, we propose a simplified mathematical model able to take into account vasoconstriction and vasodilatation phenomena to reproduce the ROTSAC experiment (i.e., the Rodent Oscillatory Tension Set-Up to study Arterial Compliance). The calibration of the model allows to directly estimate the mechanical properties of the vessel (such as the Young modulus, active pre-stress, etc.). The objective of this work is to numerically reproduce the experimental data and to understand the mechanisms involved in the impact of the drug on the mechanical behaviour of the vessel. During the ROTSAC experiments, an aortic segment is mounted between two metal hooks in an organ bath. A current source is used to control the distension force and clamp frequency of a force–length transducer. The stretch protocol was applied with physiological frequency (10 Hz) and amplitude (40 mmHg). The measured quantities are force and displacement of the aortic segment (in length and width). We build a mathematical model aiming at describing the ROTSAC experiment. The inputs of the model are the force measured in the experiment and the parameters describing the behaviour of the material (Young modulus, Poisson ratio, active pre-stress and elastic modulus, initial length, width and thickness of the aortic segment). The output of the model is the displacement (in length and width) of the aortic segment. It is important to note that these parameters are unknown and must be determined through a process known as calibration. The purpose of the calibration is estimating some unknown inputs of the model such that the outputs match the experimental measurements. The calibration procedure makes it possible to estimate the unknown model parameters from the ROTSAC static and dynamic data. Thanks to the calibration we have access to physical quantities that cannot be directly measured from the ROTSAC experiment. This paves the way for understanding the underlying mechanisms of a drug on the vessel mechanics

The Spanish version of the Three Factor Eating Questionnaire-R21 for children and adolescents (TFEQ-R21C): Psychometric analysis and relationships with body composition and fitness variables

Objective: The main purpose of the present study is to assess the factor structure and reliability of the Spanish ver sion of the 21-item Three Factor Eating Questionnaire (TFEQ-R21C) in children and adolescents and to analyze the relationships between eating behaviors, body composition and cardiovascular fitness. Subjects: A total of 192 children and adolescents took part in this study (89 boys and 103 girls; aged from 8.8 to 16.8 years old and with body mass index (BMI) ranging from 13.2 to 41.1 kg/m2 ). None of them had either a his tory of psychological or eating disorders. Measurements: Body composition (dual-energy X-ray absorptiometry-DXA), anthropometrics (body mass, height and BMI), cardiovascular fitness (cyclo-ergometer incremental test) and eating behaviors (TFEQ-R21C) were determined in all participants. Results: The confirmatory factor analysis corroborated the same three factors of the original TFEQ-R21: Uncon trolled Eating (UE), Emotional Eating (EE) and Cognitive Restraint (CR). The internal-consistency reliability (Cronbach's alpha coefficient) for the questionnaire was 0.73. Significant differences were found in BMI (F2,189 = 3.50, p = 0.032) and total fat mass (TFM) (F2,189 = 3.60, p = 0.029) between tertiles of the CR scale (children who had the lowest scores, also had lower BMI and fat mass). Cardiovascular fitness (measured by rel ative VO2 peak) differs depending on the UE and CR scores. The “healthy” group (those who were normal-weight and had also the highest relative VO2 peak) showed a significant lower CR (F3,160 = 3.07, p = 0.030) and higher UE (F3,160 = 3.86, p = 0.011) than the “unhealthy” group (those who were neither normal-weight nor had adequate relative VO2 peak). Conclusions: According to the psychometric analysis of the questionnaire, the TFEQ-R21C is a valid and useful tool to assess eating behaviors in Spanish child population. Further research is necessary to understand the links be tween eating behaviors and other health-related behaviors such as physical activity time or cardiovascular fitness

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population