Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process

Novel antimicrobial peptides against Cutibacterium acnes designed by deep learning

Abstract The increasing prevalence of antibiotic resistance in Cutibacterium acnes (C. acnes) requires the search for alternative therapeutic strategies. Antimicrobial peptides (AMPs) offer a promising avenue for the development of new treatments targeting C. acnes. In this study, to design peptides with the specific inhibitory activity against C. acnes, we employed a deep learning pipeline with generators and classifiers, using transfer learning and pretrained protein embeddings, trained on publicly available data. To enhance the training data specific to C. acnes inhibition, we constructed a phylogenetic tree. A panel of 42 novel generated linear peptides was then synthesized and experimentally evaluated for their antimicrobial selectivity and activity. Five of them demonstrated their high potency and selectivity against C. acnes with MIC of 2–4 µg/mL. Our findings highlight the potential of these designed peptides as promising candidates for anti-acne therapeutics and demonstrate the power of computational approaches for the rational design of targeted antimicrobial peptides

Effects of occupational exposure to noise and dust on blood pressure in Chinese industrial workers

Along with the rapid development of economy and urbanization, noise and air pollution are becoming major occupational health hazards in the process of industrial production. In this study, we collected data from 7293 industrial workers in China. The association between occupational exposure of noise and dust and blood pressure was investigated. Controlling for demographic variables, including sex, age, and length of service, a stepwise regression model with backward elimination was constructed. The results showed that both noise and dust decreased the level of systolic blood pressure (p < 0.001). This finding prompted the manufacturing industry to reduce noise and dust hazards and protect the occupational health of workers. Prospective studies in different populations are still required to verify the net contribution of noise and dust to the decrease in blood pressure

Photoelectrochemical Sensor for H₂S Based on a Lead-Free Perovskite/Metal–Organic Framework Composite

Halide perovskites have emerged as a highly promising class of photoelectric materials. However, the application of lead-based perovskites has been hindered by their toxicity and relatively weak stability. In this work, a composite material comprising a lead-free perovskite cesium copper iodide (CsCu2I3) nanocrystal and a metal–organic framework (MOF-801) has been synthesized through an in situ growth approach. The resulting composite material, denoted as CsCu2I3/MOF-801, demonstrates outstanding stability and exceptional optoelectronic characteristics. MOF-801 may serve a dual role by acting as a protective barrier between CsCu2I3 nanocrystals and the external environment, as well as promoting the efficient transfer of photogenerated charge carriers, thereby mitigating their recombination. Consequently, CsCu2I3/MOF-801 demonstrates its utility by providing both stability and a notably high initial photocurrent. Leveraging the inherent reactivity between H2S and the composite material, which results in the formation of Cu2S and structural alteration, an exceptionally sensitive photoelectrochemical sensor for H2S detection has been designed. This sensor exhibits a linear detection range spanning from 0.005 to 100 μM with a remarkable detection limit of 1.67 nM, rendering it highly suitable for precise quantification of H2S in rat brains. This eco-friendly sensor significantly broadens the application horizon of perovskite materials and lays a robust foundation for their future commercialization