Objective:To assess the effect of joint exposure to multiple air pollutants on sleep structure in patients with stable chronic obstructive pulmonary disease (COPD), identify key air pollutants, and analyze potential influencing factors.Methods:In this panel study, 92 stable COPD patients were recruited. From March 2021 to September 2023 in Beijing, all participants completed 254 nights of sleep monitoring. The total sleep duration, light sleep duration, deep sleep duration and rapid eye movement sleep duration and their respective proportions in total sleep duration were recorded. The exposure levels of fine particulate matter (PM 2.5), inhalable particulate matter (PM 10), nitrogen dioxide (NO 2), ozone (O 3), sulfur dioxide (SO 2), and carbon monoxide (CO) were estimated based on the infiltration factor method and time-activity logs of participants. To assess the lag effect of air pollutants, moving average concentrations of air pollutants from 0-1 day to 0-3 months were calculated. The linear mixed-effect model and Bayesian kernel machine regression (BKMR) model were used to assess the single and joint effects of air pollutants on sleep structure parameters in COPD patients, respectively. Results:All six types of air pollutants were associated with changes in sleep structure, manifesting as an increase in total sleep duration and light sleep proportion and a reduction in deep sleep proportion. The effects of O 3 were strongest at lag 0-6 days, while other air pollutants were at lag 0-3 months. Joint exposure to multiple air pollutants exerted significant joint effects on sleep structure, and NO 2 was identified as the dominant pollutant. NO 2 had a posterior inclusion probability (PIP) greater than 0.5 for light sleep proportion (PIP=0.691) and deep sleep proportion (PIP=0.957). With an interquartile range (IQR) increase of 8.6 μg/m 3 in NO 2 at lag 0-3 months, the light sleep proportion increased by 10.5% (95% CI: 2.2%-19.4%), and the deep sleep proportion decreased by 19.5% (95% CI:-30.6%- -6.8%). Conclusion:Joint exposure to air pollutants is associated with changes in sleep structure in stable COPD patients, and NO 2 may be a key pollutant.

Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Objective:To assess the effect of joint exposure to multiple air pollutants on sleep structure in patients with stable chronic obstructive pulmonary disease (COPD), identify key air pollutants, and analyze potential influencing factors.Methods:In this panel study, 92 stable COPD patients were recruited. From March 2021 to September 2023 in Beijing, all participants completed 254 nights of sleep monitoring. The total sleep duration, light sleep duration, deep sleep duration and rapid eye movement sleep duration and their respective proportions in total sleep duration were recorded. The exposure levels of fine particulate matter (PM 2.5), inhalable particulate matter (PM 10), nitrogen dioxide (NO 2), ozone (O 3), sulfur dioxide (SO 2), and carbon monoxide (CO) were estimated based on the infiltration factor method and time-activity logs of participants. To assess the lag effect of air pollutants, moving average concentrations of air pollutants from 0-1 day to 0-3 months were calculated. The linear mixed-effect model and Bayesian kernel machine regression (BKMR) model were used to assess the single and joint effects of air pollutants on sleep structure parameters in COPD patients, respectively. Results:All six types of air pollutants were associated with changes in sleep structure, manifesting as an increase in total sleep duration and light sleep proportion and a reduction in deep sleep proportion. The effects of O 3 were strongest at lag 0-6 days, while other air pollutants were at lag 0-3 months. Joint exposure to multiple air pollutants exerted significant joint effects on sleep structure, and NO 2 was identified as the dominant pollutant. NO 2 had a posterior inclusion probability (PIP) greater than 0.5 for light sleep proportion (PIP=0.691) and deep sleep proportion (PIP=0.957). With an interquartile range (IQR) increase of 8.6 μg/m 3 in NO 2 at lag 0-3 months, the light sleep proportion increased by 10.5% (95% CI: 2.2%-19.4%), and the deep sleep proportion decreased by 19.5% (95% CI:-30.6%- -6.8%). Conclusion:Joint exposure to air pollutants is associated with changes in sleep structure in stable COPD patients, and NO 2 may be a key pollutant.

Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Allergen specific immunotherapy (AIT) is to identify the patient's allergen, give the patient repeated exposure to the allergen extract, and gradually increase the concentration and dose until the target maintenance dose is reached, so that the patient can develop tolerance to the allergen, which is the only treatment that can regulate the pathogenesis of allergic diseases and change its natural course. In recent years, domestic and foreign scholars have made great progress in the clinical practice and research field of AIT. This article reviewed the relevant progress of the mechanism, efficacy and drug administration of AIT.

Allergen specific immunotherapy(AIT)is to identify the patient's allergen,give the patient repeated exposure to the allergen extract,and gradually increase the concentration and dose until the target maintenance dose is reached,so that the patient can develop tolerance to the allergen,which is the only treatment that can regulate the pathogenesis of allergic diseases and change its natural course.In recent years,domestic and for-eign scholars have made great progress in the clini-cal practice and research field of AIT.This article re-viewed the relevant progress of the mechanism,ef-ficacy and drug administration of AIT.

Allergen specific immunotherapy(AIT)is to identify the patient's allergen,give the patient repeated exposure to the allergen extract,and gradually increase the concentration and dose until the target maintenance dose is reached,so that the patient can develop tolerance to the allergen,which is the only treatment that can regulate the pathogenesis of allergic diseases and change its natural course.In recent years,domestic and for-eign scholars have made great progress in the clini-cal practice and research field of AIT.This article re-viewed the relevant progress of the mechanism,ef-ficacy and drug administration of AIT.