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Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms.The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.
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Increase in invasive group A streptococcal () infections (iGAS) in young children in the Netherlands, 2022.In 2022, a sevenfold increase in the number of notifiable invasive Streptococcus pyogenes (iGAS) infections among children aged 0-5 years was observed in the Netherlands compared with pre-COVID-19 pandemic years. Of 42 cases in this age group, seven had preceding or coinciding varicella zoster infections, nine were fatal. This increase is not attributable to a specific emm type. Vigilance for clinical deterioration as iGAS sign is warranted in young children, especially those with varicella zoster infection.
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The contribution of the alternative pathway in complement activation on cell surfaces depends on the strength of classical pathway initiation.We evaluated the contribution of the AP to complement responses initiated through the CP on human RBCs by serum of AIHA patients and recombinant antibodies. Moreover, we studied complement activation on Neisseria meningitidis and Escherichia coli. The effect of the AP was examined using either AP-depleted sera or antibodies against factor B and factor D.
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Development of a quantitative structure-activity relationship model for predicting quantum yield of hydroxyl radical generation from organic compounds.Organic compounds are capable of generating hydroxyl radicals (˙OH) through their excited triplet states in natural water. It is of significance to reveal the underlying mechanism of the generation and obtain the generation quantum yield of ˙OH from organic compounds for better understanding of its involvement in indirect photochemical processes in the environment. In this study, the ˙OH quantum yields (Φ˙OH) of 20 organic compounds were determined by photochemical experiments. The calculated Φ˙OH values for the selected organic compounds vary from (1.2 ± 0.39) × 10-5 to (7.2 ± 0.16) × 10-4. A quantitative structure-activity relationship (QSAR) model for log Φ˙OH was developed and the established model was proven to have a proper goodness of fit, robustness, and predictive ability. The QSAR model was successfully used to predict the Φ˙OH value of organic pollutants. Mechanistic interpretation showed that the electron distribution and the electronegativity of organic compounds are the most important factors that determine the generation of ˙OH. The results are helpful for understanding the generation mechanism of ˙OH from organic compounds and also provide insights into the generation of ˙OH from dissolved organic matter in natural water.