Driving under drugs in Switzerland : a descriptive cross-sectional study

Objectives: Many drugs, both illicit or for medication, are known to influence driving abilities and increase risks of accidents. We explored the prevalence of psychoactive substances in a random sample of drivers in Switzerland. Methods: Saliva samples from 1078 random drivers were collected at 24 different locations in Western Switzerland from October 2006 to April 2008 for complete toxicological analysis using liquid chromatography/tandem mass spectrometry. Results: Provisional results are available for 437 drivers. 6.2% (CI95% 4.1 to 8.9) were under the influence of illicit drugs and 8.7% under psychoactive medication (CI95% 6.2 to 11.7). 37 drivers (8.5%) were under the influence of alcohol of which 14 (3.2%) were above 0.8 mg/L. 21 drivers (4.8%) were under the combined influence of more than one psychoactive substance; however only 4 drivers (0.9%) were under both the influence of medication and alcohol. Looking more specifically at illicit substances, 22 (5.0%) were positive to cocaine, 5 (1.1%) to cannabis, and 2 (0.5%) to amphetamines ; for psychoactive medication, 17 (3.9%) were positive to benzodiazepines, 16 (3.7%) to antidepressors, 7 (1.6%) to opiates, 7 (1.6%) to neuroleptics, and 3 (0.7%) to other substances influencing driving abilities. 17/21 drivers did not self-report their consumption of drugs whereas only 9/35 failed mentioning their medication. Men drivers were 3.2 times (CI95% 1.1 to 9.5) more likely to be under the influence of illicit drugs than women. Full results will be reported when laboratory data will be available in April. Conclusions: Driving under the influence of psychoactive substances is common. In Western Switzerland, prevention messages could focus on men, driving under medication or cocaine

Advanced 3D TH and THM Modeling to Shed Light on Thermal Convection in Fault Zones With Varying Thicknesses

Fault zones exhibit 3D variable thickness, a feature that remains inadequately explored, particularly with regard to the impact on fluid flow. Upon analyzing an analytic solution, we examine 3D thermal-hydraulic (TH) dynamical models through a benchmark experiment, which incorporates a fault zone with thickness variations corresponding to realistic orders of magnitude. The findings emphasize an area of interest where vigorous convection drives fluid flow, resulting in a temperature increase to 150°C at a shallow depth of 2.7 km in the thickest sections of the fault zone. Moreover, by considering various tectonic regimes (compressional, extensional, and strike-slip) within 3D thermal-hydraulic-mechanical (THM) models and comparing them to the benchmark experiment, we observe variations in fluid pressure induced by poroelastic forces acting on fluid flow within the area of interest. These tectonic-induced pressure changes influence the thermal distribution of the region and the intensity of temperature anomalies. Outcomes of this study emphasize the impact of poroelasticity-driven forces on transfer processes and highlight the importance of addressing fault geometry as a crucial parameter in future investigations of fluid flow in fractured systems. Such research has relevant applications in geothermal energy, CO2 storage, and mineral deposits

Generation and characterization of function-blocking anti-ectodysplasin A (EDA) monoclonal antibodies that induce ectodermal dysplasia.

Development of ectodermal appendages, such as hair, teeth, sweat glands, sebaceous glands, and mammary glands, requires the action of the TNF family ligand ectodysplasin A (EDA). Mutations of the X-linked EDA gene cause reduction or absence of many ectodermal appendages and have been identified as a cause of ectodermal dysplasia in humans, mice, dogs, and cattle. We have generated blocking antibodies, raised in Eda-deficient mice, against the conserved, receptor-binding domain of EDA. These antibodies recognize epitopes overlapping the receptor-binding site and prevent EDA from binding and activating EDAR at close to stoichiometric ratios in in vitro binding and activity assays. The antibodies block EDA1 and EDA2 of both mammalian and avian origin and, in vivo, suppress the ability of recombinant Fc-EDA1 to rescue ectodermal dysplasia in Eda-deficient Tabby mice. Moreover, administration of EDA blocking antibodies to pregnant wild type mice induced in developing wild type fetuses a marked and permanent ectodermal dysplasia. These function-blocking anti-EDA antibodies with wide cross-species reactivity will enable study of the developmental and postdevelopmental roles of EDA in a variety of organisms and open the route to therapeutic intervention in conditions in which EDA may be implicated

A robust genetic algorithm for learning temporal specifications from data

We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system. We assume the training set to be labeled by human experts and that we have access only to a limited amount of data, typically noisy. We provide a systematic approach to synthesize both the syntactical structure and the parameters of the temporal logic formula using a two-steps procedure: first, we leverage a novel evolutionary algorithm for learning the structure of the formula; second, we perform the parameter synthesis operating on the statistical emulation of the average robustness for a candidate formula w.r.t. its parameters. We compare our results with our previous work [9] and with a recently proposed decision-tree [8] based method. We present experimental results on two case studies: an anomalous trajectory detection problem of a naval surveillance system and the characterization of an Ineffective Respiratory effort, showing the usefulness of our work

Robustness Analysis and Behavior Discrimination in Enzymatic Reaction Networks

Characterizing the behavior and robustness of enzymatic networks with numerous variables and unknown parameter values is a major challenge in biology, especially when some enzymes have counter-intuitive properties or switch-like behavior between activation and inhibition. In this paper, we propose new methodological and tool-supported contributions, based on the intuitive formalism of temporal logic, to express in a rigorous manner arbitrarily complex dynamical properties. Our multi-step analysis allows efficient sampling of the parameter space in order to define feasible regions in which the model exhibits imposed or experimentally observed behaviors. In a first step, an algorithmic methodology involving sensitivity analysis is conducted to determine bifurcation thresholds for a limited number of model parameters or initial conditions. In a second step, this boundary detection is supplemented by a global robustness analysis, based on quasi-Monte Carlo approach that takes into account all model parameters. We apply this method to a well-documented enzymatic reaction network describing collagen proteolysis by matrix metalloproteinase MMP2 and membrane type 1 metalloproteinase (MT1-MMP) in the presence of tissue inhibitor of metalloproteinase TIMP2. For this model, our method provides an extended analysis and quantification of network robustness toward paradoxical TIMP2 switching activity between activation or inhibition of MMP2 production. Further implication of our approach is illustrated by demonstrating and analyzing the possible existence of oscillatory behaviors when considering an extended open configuration of the enzymatic network. Notably, we construct bifurcation diagrams that specify key parameters values controlling the co-existence of stable steady and non-steady oscillatory proteolytic dynamics

Evaluation of postmortem measurement of NT-proBNP as a marker for cardiac function.

Clinical biomarkers of cardiac function could also be monitored postmortem. Among the natriuretic peptides, the aminoterminal portion of pro-brain natriuretic peptide (NT-proBNP) appears to be a more reliable postmortem tool than the BNP, owing to its longer half-life and greater stability. In living persons, NT-proBNP is considered to be a marker of heart failure, and its level rises after cardiac ischemia. The goal of this study was first to evaluate the postmortem stability of NT-proBNP, then to measure the NT-proBNP levels in postmortem cases of heart failure related to coronary ischemia. The goal of this study was also to evaluate the correlations between different specimens collected at autopsy (e.g. blood, serum, vitreous humor and pericardial fluid). The study included 96 cases, which were classified into 4 groups according to the autopsy and histological findings. The NT-proBNP levels were significantly higher in individuals who had suffered from chronic cardiac ischemia, with or without acute coronary events, than in either control cases or those who had suffered from acute thromboembolism or acute rupture of a plaque without chronic cardiac ischemia. The highest levels were registered in individuals who had suffered from acute coronary thromboembolism in association with chronic coronary ischemia. Good correlations in the NT-proBNP levels for the different specimens were observed between samples of femoral blood, serum, and pericardial fluid. Our data indicated that postmortem measurements of NT-proBNP are reliable and compatible with clinical findings

Describing adverse events in medical inpatients using the Global Trigger Tool

AIMS: The purpose of the study was to describe the type, prevalence, severity and preventability of adverse events (AEs) that affected hospitalised medical patients. We used the previously developed and validated Global Trigger Tool from the Institute for Healthcare Improvement.METHODS: Using an adapted version of the Global Trigger Tool, we conducted a retrospective chart review of adult patients hospitalised in five medical wards at a university hospital in Switzerland. We reviewed a random sample of 20 patients' charts for a total study period of 12 months (September 2016 to August 2017). Two trained nurses searched independently for triggers and possible AEs. All AEs were further validated by a senior physician. The number of triggers and AEs detected, as well as the severity and preventability of each, was assessed and analysed using descriptive statistics.RESULTS: From a sample of 240 patient charts, we identified 1371 triggers and 336 AEs in 144 (60%) inpatients. This translates to an AE rate of 95.7 AEs per 1000 patient days. Most AEs (86.1%) caused temporary harm to the patient and required an intervention and/or prolonged hospitalisation. The estimated preventability of the in-hospital AEs was 29%. Healthcare-associated infections (25.8%) and neurological reactions (22.9%) were the most frequent AE types.CONCLUSION: We found that about two thirds of patients suffered from AEs with harm during hospitalisation. It is common knowledge that AEs occur in hospitals and that they have potentially harmful consequences for patients, as well as a strong economic impact. However, to adequately prioritise patient safety interventions, it is essential to explore the nature, prevalence, severity and preventability of AEs. This is not only beneficial for the patients, but also cost effective in terms of shorter hospital stays

Health Industries in the Twentieth Century. Introduction

This article is the introduction to the special issue' Health Industries in the Twentieth Century'. It offers a broad literature review of scholarly works about the history of health and medicine, and stresses the opportunities for business historians to tackle the field of healthcare

Conformance-based doping detection for cyber-physical systems

We present a novel and generalised notion of doping cleanness for cyber-physical systems that allows for perturbing the inputs and observing the perturbed outputs both in the time– and value–domains. We instantiate our definition using existing notions of conformance for cyber-physical systems. We show that our generalised definitions are essential in a data-driven method for doping detection and apply our definitions to a case study concerning diesel emission tests

On Robustness Computation and Optimization in BIOCHAM-4

Long version with appendicesInternational audienceBIOCHAM-4 is a tool for modeling, analyzing and synthesizing biochemical reaction networks with respect to some formal, yet possibly imprecise, specification of their behavior. We focus here on one new capability of this tool to optimize the robustness of a parametric model with respect to a specification of its dynamics in quantitative temporal logic. More precisely, we present two complementary notions of robustness: the statistical notion of model robustness to parameter perturbations, defined as its mean functionality, and a metric notion of formula satisfaction robustness, defined as the penetration depth in the validity domain of the temporal logic constraints. We show how the formula robustness can be used in BIOCHAM-4 with no extra cost as an objective function in the parameter optimization procedure, to actually improve the model robustness. We illustrate these unique features with a classical example of the hybrid systems community and provide some performance figures on a model of MAPK signalling with 37 parameters