Humans are exposed to thousands of chemicals, including environmental chemicals. Unfortunately, little is known about their potential toxicity, as determining the toxicity remains challenging due to the substantial resources required to assess a chemical in vivo. Here, we present a novel hybrid neural network (HNN) deep learning method, called HNN-Tox, to predict chemical toxicity at different doses. To develop a hybrid HNN-Tox method, we combined two neural network frameworks, the Convolutional Neural Network (CNN) and the multilayer perceptron (MLP)-type feed-forward neural network (FFNN). Combining the CNN and FCNN in the field of environmental chemical toxicity prediction is a novel approach. We developed several binary and multiclass classification models to assess dose-range chemical toxicity that is trained based on thousands of chemicals with known toxicity. The performance of the HNN-Tox was compared with other machine-learning methods, including Random Forest (RF), Bootstrap Aggregation (Bagging), and Adaptive Boosting (AdaBoost). We also analyzed the model performance dependency on varying features, descriptors, dataset size, route of exposure, and toxic dose. The HNN-Tox model, trained on 59,373 chemicals annotated with known LD50 and routes of exposure, maintained its predictive ability with an accuracy of 84.9% and 84.1%, even after reducing the descriptor size from 318 to 51, and the area under the ROC curve (AUC) was 0.89 and 0.88, respectively. Further, we validated the HNN-Tox with several external toxic chemical datasets on a large scale. The HNN-Tox performed optimally or better than the other machine-learning methods for diverse chemicals. This study is the first to report a large-scale prediction of dose-range chemical toxicity with varying features. The HNN-Tox has broad applicability in predicting toxicity for diverse chemicals and could serve as an alternative methodology approach to animal-based toxicity assessment.

Humans are exposed to thousands of potentially toxic chemicals including environmental chemicals such as industrial wastes, food products, solvents, air pollutants, fertilizers, pesticides, insecticides, carcinogens, drugs, metals/metalloids, and other industrial chemicals. Approximately 300,000 such chemicals currently in use, unfortunately little is known about their potential toxicity. Determining human toxicity potential of chemicals remains a challenge due to a substantial resource required to assess a chemical in-vivo, and only a few thousand single chemicals in commercial use has been evaluated. In this study, to predict the environmental chemical toxicity, we developed a new hybrid neural network (HNN) deep learning model consisting of a Convolutional Neural Network (CNN) and multilayer perceptron (MLP) type feed forward neural network (FFNN). Our HNN deep learning model trained based on thousands of chemicals, presented the best performance for majority of the cases. Taken together, our hybrid HNN deep learning models has a wide applicability in the prediction of toxicity of any chemical category and its mixtures.

Background Cerebral sinus venous thrombosis (CSVT) is one of the many side effects encountered during acute lymphoblastic leukemia (ALL) therapy. Due to the rarity of cases, lack of data, and consensus management, no recommendations exist to target the population at risk. Methods This is a retrospective chart review of 229 consecutive patients diagnosed with ALL with an age range of 1–21 years, treated at the Children's Cancer Center of Lebanon between October 2007 and February 2018. Results The incidence of CSVT was 10.5%. Using univariate analysis, increased risk of CSVT was observed with male gender, age >10 years, T‐cell immunophenotype, intermediate/high‐risk disease, maximum triglyceride (TG) level of >615 mg/dl, presence of mediastinal mass, and larger body surface area (BSA). With multivariate analysis, the only statistically significant risk factors were maximum TG level, BSA, presence of mediastinal mass, and risk stratification (intermediate/high risk). Conclusion Our study was able to unveil TG level of >615 mg/dl, mediastinal mass, and a larger BSA as novel risk factors that have not been previously discussed in the literature.

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Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering. Despite many promising applications in clinical medicine, adoption of these models in real-world settings has been largely limited by their tendency to generate incorrect and sometimes even toxic statements. In this study, we develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios (n = 130) evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality (mean of 18% at p-value < 0.05) across all specialties, with improvements in completeness and safety. Our results demonstrate the potential for large language models to be effective tools in the clinical decision-making process, while also emphasizing the impor- tance of careful testing and deployment to mitigate their shortcomings.