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Bisphenol S (BPS) derivatives have potential reproductive developmental toxicity and have been found in the environment and in breast milk. The level of infant exposure and the source are currently unknown. In this study, we investigated BPS and six derivatives (together referred to as BPs) in urine samples from mothers and infants, indoor dust, breast milk and infant formula in Beijing, China. BPS, diphenyl sulfone (DPS) and 4-allyloxy-4′-hydroxydiphenyl sulfone (BPS-MAE) were the main BPs. Notably, the concentration of DPS in infants’ urine was higher than that of BPS, which warrants attention. Infants have higher daily intake levels than mothers. Exclusively breastfed infants have a higher risk of BPs exposure than exclusively formula-fed infants. For exclusive breastfed infants, the contribution of individual BPs through breast milk was 23.2% to 93.6%. While for exclusively formula-fed infants, the contribution of individual BPs through infant formula was 30.5% to 70.3%. The contribution of individual BPs through indoor dust was no more than 10%. The results suggesting that infants can be exposed to BPs through other pathways. This is the first comprehensive assessment of maternal and infants exposure to BPS derivatives, providing insights into the sources of infant exposure.

AbstractBoth insufficient and excessive iodine intake can lead to thyroid-related disorders. Although China has made progress in eliminating iodine deficiency over the past few decades, the incidence of thyroid cancer is increasing. Currently, there is a lack of relevant research on the tradeoff between the benefits and risks of salt iodization in China. In this study, we developed a method that combines the total probability algorithm and disease burden to evaluate the appropriate amount of salt iodization. Following the principle of minimizing the comprehensive disease burden and using the metabolic model of human iodine nutrition. Based on the average national iodine level in water, the optimal iodine content in Chinese salt is determined to be 17 mg/kg. However, iodine content in water is not evenly distributed in China. Approximately 3.23% of administrative villages have water iodine concentrations exceeding 80 ug/L, eliminating the need for iodine fortification in salt. Approximately 83.51% of administrative villages need to continue implementing the salt iodization policy, with the optimal iodine content in salt ranging from 15 to 18 mg/kg. In 13.16% of administrative villages, the iodine content in salt is determined based on the local water iodine concentration, ranging from 0 to 15 mg/kg. Our study cracks open a window of insight suggesting that the optimal iodine content for salt is lower than the existing benchmark dictated by the prevailing policy in China. Hence, there is an urgent need to refine and advance the iodine supplementation strategy in salt to pave the way for precision medicine and health-centric iodine supplementation strategies. [ABSTRACT FROM AUTHOR]

Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications. Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products, and is time-consuming and labor-intensive. Recently, machine learning and artificial intelligence algorithms have been applied to retention time prediction, which show unparalleled advantages over traditional experimental methods. However, existing retention time prediction methods usually suffer from the scarcity of comprehensive training datasets, sparsity of valid data, and lack of classification in datasets, resulting in poor generalization capability and accuracy. In this study, a dataset for 10,905 compounds was constructed including their retention times. Next, an innovative classification system was implemented, classifying 10,905 compounds into a 3-tier hierarchy across 141 classes, based on functional group weighting. Then, data augmentation was performed within each category using simplified molecular input line entry system (SMILES) enumeration combined with structural similarity expansion. Finally, by training the optimal quantitative structure-retention relationship (QSRR) models for each category of compounds and selecting the best-fitting model for prediction via discriminant analysis during the prediction period, a novel and universal high-throughput retention time prediction model was established. The results demonstrate that this model achieves an R 2 of 0.98 and an average prediction error of 23 s, outperforming currently published models. This study provides a scientific basis for high throughput and rapid prediction of unknown pollutants, data mining, nontargeted screening, etc., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2025 Mengdie Fan et al.)