The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)‐based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI‐based methodologies for the fulfillment of regulatory‐related toxicological issues.
This article is categorized under:
Data Science > Chemoinformatics
Data Science > Artificial Intelligence/Machine Learning
There is little doubt that increases in thermal load beyond the thermo-neutral state prove progressively stressful to all living organisms. Increasing temperatures across the globe represent in some locales, and especially for outdoors workers, a significant source of such chronic load increase. However, increases in thermal load affect cognition as well as physical work activities. Such human cognition has perennially been viewed as the primary conduit through which to solve many of the iatrogenic challenges we now face. Yet, thermal stress degrades the power to think. Here, we advance and refine the isothermal description of such cognitive decrements, founded upon a synthesis of extant empirical evidence. We report a series of mathematical functions which describe task-specific patterns of performance deterioration, linking such degrees of decrement to the time/temperature conditions in which they occur. Further, we provide a simple, free software tool to support such calculations so that adverse thermal loads can be monitored, assessed and (where possible) mitigated to preserve healthy cognitive functioning.
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