Objective:To analyze the correlation between obstructive sleep apnea (OSA) and attention deficit hyperactivity disorder (ADHD).Methods:The clinical Data, polysomnography (PSG) and cognitive function examination results of 112 OSA children admitted to Department of Otorhinolaryngology Head and Neck Surgery of the Second Affiliated Hospital of Xi′an Jiaotong University from January 2019 to June 2020 were retrospectively analyzed. According to the severity of OSA, the children were divided into mild, moderate and severe OSA groups, and the basic demographic characteristics, sleep parameters and ADHD occurrence were analyzed. According to the results of ADHD examination, the children were divided into ADHD group and non-ADHD group, and the basic demographic characteristics and sleep parameters were analyzed. Taking these parameters as independent variables, binary Logistic regression analysis was conducted to establish the model equation for predicting the risk of OSA associated ADHD among children.Results:Grouped by OSA severity, among the three groups, apnea-hypopnea index (AHI) [3.70 (2.84, 5.47) vs 8.59 (7.50, 9.54) vs 19.48 (15.83, 25.23)], obstructive apnea index (OAI) [1.31 (0.93, 1.82) vs 3.03 (1.54, 4.41) vs 11.69 (8.53, 15.42)], obstructive apnea-hypopnea index (OAHI) [2.82 (1.81, 3.64) vs 6.17 (5.58, 7.26) vs 15.68 (13.12, 21.25)], and respiratory event-related arousal index [0.50 (0.25, 1.05) vs 1.25 (0.70, 2.23) vs 2.40 (1.60, 4.70)] increased, minimum pulse oxygen saturation (SpO 2) [90.00 (88.00, 92.00) vs 87.00 (83.00, 90.25) vs 81.00 (76.00, 85.00)] decreased, the differences were statistically significant (all P<0.05). The non-rapid eye movement (NREM)1 period time ratio of the severe OSA group was significantly longer than that of the mild OSA group, while the average SpO 2 was significantly lower than that of the mild OSA group; the NREM3 period time ratio of the moderate and severe OSA group was significantly less than that of the mild OSA group; the arousal index of the severe OSA group was significantly greater than the mild or moderate OSA group. There were no statistically significant differences among the three groups in gender, age, body mass index, sleep efficiency, rapid eye movement (REM) period time ratio, and NREM2 period time ratio (all P>0.05). Mild OSA group had 10 cases of ADHD (17.54%), moderate OSA group had 7 cases (23.33%) of ADHD, severe OSA group had 9 cases of ADHD (36.00%), and the difference was not statistically significant. Grouped by ADHD examination, the AHI, OAI, OAHI, and NREM1 period time ratios of the ADHD group were significantly higher than those of the non-ADHD group, while the sleep efficiency, minimum SpO 2 and NREM3 period time ratio were significantly lower than those of the non-ADHD group. The Logistic regression analysis suggested that ADHD was correlated with sleep efficiency, minimum SpO 2, and NREM3 period time.The established Logistic regression equation was: X=15.670+0.061×(sleep efficiency)-0.212×(minimum SpO 2)-0.144×(NREM3 period time ratio), the sensitivity and specificity of the model prediction were 84.6% and 79.1% respectively when the area under the receiveroperating characteristic curves was 0.867. Conclusions:OSA and ADHD in children have a certain correlation. Sleep structure disturbance and intermittent hypoxia may be important reasons. The predictive model equations obtained by PSG in this study can be used to assess the risk of ADHD in children with OSA.

Background Noise has multiple negative effects on the organism, and gut microbes are influenced by the environment and are closely associated with the development of diseases. Currently, the effects of chronic noise exposure on intestinal microbiota are poorly understood. Objective To investigate the effects of noise exposure on the structure of rat gut microbiota and to make predictions of gut microbiota function. Methods Male Wistar rats (6 weeks old, 160-180 g) were randomly divided into control, NE_95dB, and NE_105dB groups, 10 rats in each group. Rats in the NE_95dB and the NE_105dB groups were exposed to noise at 95 dB sound pressure level (SPL) and 105 dB SPL, respectively, 4 h per day for consecutive 30 d, while the control group was exposed to background noise. Feces were collected after the last noise exposure for intestinal microbiota detection. Based on the 16S ribosomal RNA (rRNA) gene sequencing method, the diversity and structure of microbiota in rat intestinal contents were analyzed and compared. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was applied to predict functions of the identified intestinal microbiota genes. Results Significant differences were found in the microbial structure of the rat gut after the designed noise exposure. In the α diversity results, there was a statistically significant difference in the Chao1 index between the NE_95dB group and the NE_105dB group (P=0.02), while there were no statistically significant differences in the Shannon and Simpson indexes between the noise exposure groups and the control group (P>0.05). The β diversity analysis results showed significant differences in species abundance between the control group and the noise exposure groups (P=0.001). Further species analysis results showed that the relative abundances of the Ruminococcaceae_NK4A214_group (P<0.05) and Peptococcaceae_unclassified (P<0.01) at the genus level were significantly higher in the NE_105dB group, and the relative abundance of Parasutterella (P<0.05) was significantly higher in the NE_95dB group compared to the control group. In addition, the Ruminococcaceae_NK4A214_group (P<0.05) was also significantly higher in the NE_105dB group compared to the NE_95dB group. The PICRUSt functional prediction analysis results showed that there were eight differential pathways between the control group and the NE_95dB group, in which D-arginine and D-ornithine metabolism, ascorbate and aldarate metabolism, carotenoid biosynthesis, glycerophospholipid metabolism, mineral absorption, NOD-like receptor signaling pathway and non-homologous end-joining were significantly down-regulated, and nucleotide metabolism was significantly up-regulated. There were 38 differential pathways between the control group and the NE_105dB group. Among them, D-arginine and D-ornithine metabolism, and mineral absorption were the differential metabolic pathways in both noise exposure groups, and both were down-regulated relative to the control group. Conclusion Chronic noise exposure could alter structure of rat gut microbiota and may affect metabolic functions of multiple microbiota genes.

Background Noise can cause not only auditory system injury, but also liver damage. However, the biomarkers and pathological mechanism of noise-induced liver injury are not clear yet. Objective To observe the effect of noise on the morphological structure and functions of rat liver. Methods A total of 30 Wistar rats were randomly divided into a normal control group, a low noise exposure group [(95 dB sound pressure level (SPL)], and a high noise exposure group (105 dB SPL). After 30 days of noise exposure, blood was collected, and livers were harvested and fixed. The pathological changes of livers were observed. The levels of biochemical indicators of liver function, blood glucose, and blood lipid were measured. Serum metabolites were detected by ultra-high-pressure liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS). Differential metabolite markers and metabolic pathways were identified. Results Compared with the control group, the body weight gain decreased in the low noise group and the high noise group after noise exposure (P<0.001, P<0.05). The pathological results showed that noise caused the rat livers’ morphological and structural damage at various degrees, and damage of the high noise exposure group was more serious. Compared with the control group, the serum levels of aspartate aminotransferase, albumin, and glycosylated serum protein in the low noise exposure group were increased (P<0.05), but the total bile acid level was decreased (P<0.05). The serum levels of alanine aminotransferase, aspartate aminotransferase, albumin, triglyceride, low density lipoprotein, and glycosylated serum protein in the high noise group exposure were increased (P<0.05), but the glucose level was decreased (P<0.05). In the serum metabolomics analysis, 11 differential metabolites were screened out in the low noise exposure group, which were mainly enriched in 3 pathways (thiamine metabolism, primary bile acid biosynthesis, and bile secretion) related to liver metabolism. Four differential metabolites were screened out in the high exposure noise group, which were mainly enriched in four significantly different metabolic pathways (insulin signaling pathway, non-alcoholic fatty liver disease, bile secretion, and insulin secretion). All the metabolic pathways involved in bile acid secretion and metabolism. Conclusion Nosie exposure can not only damage the liver structure of rats, but also affects the metabolism functions of liver. The mechanism may be related to bile acid secretion metabolic pathway.