Chronic and subchronic toxicity are very important endpoints for evaluating the long-term and medium-term toxicity of chemical substances. However, due to the complex mechanism and diverse chemical structures, developing effective computer models rem... read more
IMPORTANCE: Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from routine pathology slides. Although these approaches could accelerate treatment decisions in lung cancer,... read more
Immigrants face a unique challenge in translating their home country human capital to secure employment in their host country's labor market, potentially leading to underemployment. In this integrative conceptual review, we formalize a framework to e... read more
Clinical psychology is a discipline reliant on self-reports but uniquely susceptible to specific biases associated therewith. Here we provide a prototype for objective behavioral assessment drawn from the field of alcohol science, reviewing the resea... read more
BACKGROUND: Idiosyncratic DILI is a complex clinical challenge requiring timely and accurate decision support. LiverTox, curated by the National Institute of Health (NIH), offers a comprehensive DILI evidence base, but its encyclopedia-like format hi... read more
The reaction O+(4Su) + N2(X1Σg+) → NO+(X1Σ+) + N(4Su) is a critical process both in Earth's ionosphere and in high-temperature nonequilibrium flows surrounding hypersonic vehicles. However, state-resolved dynamical investigations of this process have... read more
Proceedings of the National Academy of Sciences of the United States of America
Feb 12, 2026
Forecast models are a key decision-support tool for public health authorities in managing epidemics, feeding into early warning systems, scenario evaluations, and an empirical basis for resource allocation. In Brazil, improving dengue forecasting bec... read more
Journal of chemical theory and computation
Feb 12, 2026
Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present a novel neural network architecture, MDtrajNet, and a pretrained... read more
This study aimed to develop and validate a machine learning-based model for predicting 24-hour mortality in critically ill patients using prehospital and admission clinical data. We conducted a retrospective cohort study leveraging data from the preh... read more
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