Development of novel testing strategies to detect adverse human health effects is of interest to replace in vivo-based drug and chemical safety testing. The aim of the present study was to investigate whether physiologically based kinetic (PBK) modeling-facilitated conversion of in vitro toxicity data is an adequate approach to predict in vivo cardiotoxicity in humans. To enable evaluation of predictions made, methadone was selected as the model compound, being a compound for which data on both kinetics and cardiotoxicity in humans are available. A PBK model for methadone in humans was developed and evaluated against available kinetic data presenting an adequate match. Use of the developed PBK model to convert concentration–response curves for the effect of methadone on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) in the so-called multi electrode array (MEA) assay resulted in predictions for in vivo dose–response curves for methadone-induced cardiotoxicity that matched the available in vivo data. The results also revealed differences in protein plasma binding of methadone to be a potential factor underlying variation between individuals with respect to sensitivity towards the cardiotoxic effects of methadone. The present study provides a proof-of-principle of using PBK modeling-based reverse dosimetry of in vitro data for the prediction of cardiotoxicity in humans, providing a novel testing strategy in cardiac safety studies.
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Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.
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When firefighting, the combination of exposition to high temperatures, high physical demands and wearing (heavy and insulated) personal protective equipment lead to increased risk of heat stress and exhaustion in firefighters. Heat stress can easily evolve into a life-threatening heat stroke. Once heat stress occurred, the chance of getting another heat stroke during deployment gets higher. Moreover, intermittent exposure to heat stress over several years, is a risk factor for heart diseases. Similarly, exhausted during a deployment, a firefighter needs more time to rehabilitate before he can safely be deployed again. Heat stress and exhaustion can lead to line-of-duty cardiovascular events. Therefore preventing heat stress and exhaustion during deployment is beneficial for health, functioning and employability of firefighters. Since currently available measurement of the core temperature, such as thermometer pill or neck patch thermometer, are not reliable or practical for firefighters, an alternative approach may be used, namely, estimation of the core temperature based on non-invasive observation of the heart rate. Exhaustion is estimated using the training impulse model based on the heart rate reserve. Our achievement is a MoSeS health monitor system (as a smartphone application) that can real time analyze the health status of a firefighter and predict exhaustion and heat stress during deployment. The system is cheap (only a heart rate sensor and a smartphone application is needed), easy to use (intuitive “traffic light” signal), and objective (the health status is determined based on measurements of the heart rate). The only restriction is that the developed model is strongly dependent on personal maximum and minimum heart rate which need to be established behforehand. Moses Health Monitoring system for Firefighters CC BY-NC-ND Conference Proceedings 17th international e-SOCIETY 2019 IADIS
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