Background: Ambient air pollution is closely related to a variety of health outcomes. Few studies have focused on the correlations between air pollution exposure and children’s sexual development. In this study, we investigated the potential effects of exposure to air pollution on precocious puberty among children using real-world evidence. Methods: We conducted a case-crossover study (n = 2201) to investigate the effect of ambient air pollution exposure on precocious puberty from January 2016 to December 2021. Average exposure levels of PM2.5, PM10, SO2, NO2, CO, and O3 before diagnosis were calculated by using the inverse distance weighting (IDW) method. Distributed lag nonlinear model (DLNM) was used to assess the effect of air pollutants exposure on precocious puberty. Results: The mean age of the children who were diagnosed with precocious puberty was 7.47 ± 1.24 years. The average concentration of PM2.5 and PM10 were 38.81 ± 26.36 μg/m3 and 69.77 ± 41.07 μg/m3, respectively. We found that exposure to high concentrations of PM2.5 and PM10 might increase the risk of precocious puberty using the DLNM model adjusted for the age, SO2, NO2, CO, and O3 levels. The strongest effects of the PM2.5 and PM10 on precocious puberty were observed in lag 27 (OR = 1.72, 95% CI: 1.01–2.92) and lag 16 (OR = 1.95, 95% CI: 1.33–2.85), respectively. Conclusion: Our findings supported that short-term exposure to air pollution was the risk factor for precocious puberty. Every effort should be made to protect children from air pollution.

Background: Information on the clinical characteristics and severity of autoimmune encephalitis with antibodies against the N-methyl-d-aspartate receptor (NMDAR) in children is attracting more and more attention in the field of pediatric research. Methods: In this retrospective cohort study, all cases (n = 67) were enrolled from a tertiary children's hospital, from 2017 to 2020. We compared severe cases that received intensive care unit (ICU) care with nonsevere cases that did not receive ICU care and used machine learning algorithm to predict the severity of children, as well as using immunologic and viral nucleic acid tests to identify possible pathogenic triggers. Results: Mean age of children was 8.29 (standard deviation 4.09) years, and 41 (61.19%) were girls. Eleven (16.42%) were admitted to the ICU, and 56 (83.58%) were admitted to neurology ward. Ten individual parameters were statistically significant differences between severe cases and nonsevere cases ( P < .05), including headache, abnormal mental behavior or cognitive impairment, seizures, concomitant tumors, sputum/blood pathogens, blood globulin, blood urea nitrogen, blood immunoglobulin G, blood immunoglobulin M, and number of polynucleated cells in cerebrospinal fluid. Random forest regression model presented that the overall prediction power of severity reached 0.806, among which the number of polynucleated cells in cerebrospinal fluid contributed the most. Potential pathogenic causes exhibited that the proportion of mycoplasma was the highest, followed by Epstein-Barr virus. Conclusion: Our findings provided evidence for early identification of autoimmune encephalitis in children, especially in severe cases.