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To explore the relationship between obstructive sleep apnea (OSA) and cognitive impairment by combining event-related evoked potentials (ERPs) and China-Wechsler Younger Children Scale (C-WISC) in children with sleep-disordered breathing (SDB) with vs. without OSA. This was a retrospective case-control study of all consecutive children (n = 148) with adenoid tonsil hypertrophy between July 2017 and March 2019 at the Hospital. The children were divided into the OSA (n = 102) and non-OSA (n = 46) groups. The apnea-hypopnea index (AHI), obstructive apnea index (OAI), and obstructive apnea-hypopnea index (OAHI) in the OSA group were elevated compared with those of the non-OSA group (all P < 0.001). The mean oxygen saturation (SaO 2) and SaO 2 nadir were lower in the OSA group compared with the non-OSA group (both P < 0.001). The respiratory arousal index (RAI) values in the OSA group were larger than those of the non-OSA group (P < 0.001). The P300 and N100 latencies in the OSA group were longer than those of the non-OSA group (both P < 0.001). Pearson's correlation analysis revealed correlations of the P300 peak latency with full-scale intelligence quotient (FIQ) (P < 0.001 and r = −0.527), verbal intelligence quotient (VIQ) (P < 0.001 and r = −0.448), and performance intelligence quotient (PIQ) (P < 0.001 and r = −0.515). There was a correlation between the N100 peak latency and PIQ (P = 0.026 and r = −0.183). ERPs, as an objective measurement, might help assess cognitive impairment in children with OSA. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of metro systems in urban transportation, there is a growing concern about the microbial pollution risks associated with these systems. To address this issue, this study employs metagenomic sequencing technology to investigate the distribution of Mycobacterium in aerosol samples collected from metro environments. Through the analysis of various environmental factors, insights into the factors influencing Mycobacterium contamination in metro systems are provided, aiming to offer evidence to support prevention and control measures against such pollution. In this study, a total of 90 species of Mycobacterium were detected in aerosol samples with a positivity rate of 30.77% including Mycobacterium tuberculosis that accounts for over 90% of the total abundance, as well as common opportunistic pathogens such as Mycobacterium gordonae , Mycobacterium avium , Mycobacterium intracellular , and Mycobacterium lentiflavum. Through correlation analysis, it was found that the distribution of Mycobacterium is related to season, temperature, CO 2 , PM 1 , PM 2.5 , and PM 10 concentrations. In conclusion, it is recommended to adopt measures to control temperature and airborne concentrations of CO 2 and particulate matter (PM 1 , PM 2.5 , and PM 10) in order to minimize the risk of Mycobacterium contamination in metro systems. By implementing these recommendations, the prevention and control of Mycobacterium pollution can be effectively enhanced in the context of urban metros. • A total of 90 species of Mycobacterium were detected in three metro station. • Mycobacterium tuberculosis is widely present in over 90% of the sample. • Lower temperatures, the concentration of CO 2 and PM may help reduce Mycobacterium contamination in subways. [ABSTRACT FROM AUTHOR]

Although exposure to ambient air pollution has been associated with outpatient visits for respiratory diseases, there have been few recent studies investigating the effect of the chemical components of ambient particulate matter with an aerodynamic diameter of <2.5 μm (PM 2.5) on pediatric outpatient visits for respiratory diseases. In addition, there remains scarce evidence regarding PM 2.5 components and sources that are most relevant to pediatric outpatient visits for respiratory diseases. This study aimed to determine the association between four ambient pollutants (SO 2 , NO 2 , PM 10 , and PM 2.5), sources and chemical components of PM 2.5 , and pediatric outpatient visits for respiratory diseases. This time-series study was conducted between January 1, 2018 and December 31, 2019 in Shanghai, China. A positive matrix factorization model was used to determine the source apportionment of PM 2.5 , and a generalized additive model was employed to evaluate the effect of air pollutants and the components and sources of PM 2.5 on pediatric outpatient visits for respiratory diseases. In addition, a two-pollutant model was used to investigate the potential confounding effects of the copollutants. The study revealed that a 10 μg/m3 increase in SO 2 , NO 2 , PM 10 , and PM 2.5 concentrations corresponded to increases of 3.4% (95% CI: 2.3%–4.6%), 1.9% (95% CI: 1.7%–2.2%), 0.5% (95% CI: 0.4%–0.7%), and 0.5% (95% CI: 0.4%–0.7%), respectively, in pediatric outpatient visits for respiratory diseases on the day with the most significant lag effect. Notably, Ni exposure more strongly correlated with increased pediatric outpatient visits for respiratory diseases, with the percent of increase by 10 ng/m3 ranging from 7.6% to 16.5%. Moreover, among the PM 2.5 sources researched in this study, oil and coal combustion were the most important for increasing pediatric outpatient visits for respiratory diseases. This study demonstrates that ambient pollutants and the chemical components and sources of PM 2.5 were significantly associated with an elevated risk of pediatric outpatient visits for respiratory diseases and provides insights into the differential health risks of PM 2.5 components. The Effect of Sources and Components of PM 2.5 on Pediatric Outpatient Visits for Respiratory Diseases. [Display omitted] • A PMF model was used to study the effect of PM 2.5 sources on pediatric outpatient visits for respiratory diseases (POVRD). • PM 2.5 chemical components were associated with POVRD, especially Ni exposure. • Oil combustion is potentially responsible for increased POVRD risks. [ABSTRACT FROM AUTHOR]

The Air Quality Health Index (AQHI) based on the association between excess mortality risk and air pollutants was established by Canadian scientists in 2008 and it has been widely used for predicting multiple air pollutants related health risks. However, it remains unclear whether AQHI is a better indicator in predicting other health risks like respiratory diseases for vulnerable populations. This study aimed to propose a case on constructing an AQHI based on the association between air pollution and hospital outpatient visits for respiratory diseases among children and to determine whether the index adequately predicts early risk of respiratory diseases in children. Data of air pollutants, including particulate matter of less than 2.5 μm in aerodynamic diameter (PM 2.5), sulfur dioxide (SO 2), nitrogen dioxide (NO 2), and ozone (O 3) were collected from Shanghai Environmental Monitoring Center from January 1, 2015 to December 31, 2019. Daily number of hospital outpatient visits for pediatric (0–12 years old) respiratory diseases were also obtained. Time-series analysis with a generalized additive model (GAM) during warm (Apr. to Sep.) and cool periods (Oct. to Mar.) was conducted to estimate the associations between respiratory-related hospital outpatient visits in children and the concentrations of air pollutants including PM 2.5 , SO 2 , NO 2 , and O 3 in Shanghai from 2015 to 2018. The sum of excess risk (ER) of hospital outpatient visits in warm and cool periods was used to construct the AQHI for children (AQHI c). As AQHI c was established using the data from 2015 to 2018, we examined the validity of the index with data from 2017 to 2019. We also compared the predictive power of AQHI c and the currently used Air Quality Index (AQI) with the data of daily hospital outpatient visits from 2017 to 2019. According to one- and two-pollutant models of GAM, the concentration-response coefficients of PM 2.5 , SO 2 , NO 2, and O 3 were selected to construct the AQHI c in the warm period, while only SO 2 , NO 2, and PM 2.5 were included for the construction of AQHI c in the cool period as O 3 was negatively correlated with the hospital outpatient visits. There were almost linear exposure-response correlations between AQHI c and daily hospital outpatient visits. AQHI c and AQI showed similar results with the annual data in terms of model fit statistics. When the data was divided into warm and cool periods, the power of AQHI c was slightly stronger than that of AQI in predicting the air pollution-related health risks. AQHI c we developed might comprehensively reflect the combined effects of air pollution in Shanghai and it could be a more valid prediction index for evaluating air pollution-related health risks in children. [Display omitted] • To explore concentration-response associations during warm and cool periods between air pollution and hospital outpatient visits. • To construct an index (AQHI c) basing on the sum of excess risk (ER) of hospital outpatient visits in warm and cool periods. • To compare the power of AQHI c and AQI in predicting air pollution-related health risks. [ABSTRACT FROM AUTHOR]