BACKGROUND:Understanding the characteristics and influencing factors of the gut microbiota in athletes can help determine the optimal gut microbial composition for relevant sport events.Further investigation in this area could provide important insights for improving athletic performance and recovery as well as developing personalized nutrition prescriptions.OBJECTIVE:To summarize the characteristics of gut microbiota in athletes,and to elucidate the important factors influencing the gut microbiota characteristics of athletes from the perspectives of exercise training and dietary intake.METHODS:A literature search was conducted using the PubMed,ScienceDirect,CNKI,WanFang and VIP databases for publications from 2004 to 2024.The search terms included"microbiota,microbiome,athlete,exercise,training,diet,nutrition,dietary fiber,protein,ketogenic,fat"in English and Chinese.After excluding studies of poor quality and irrelevant content,a total of 65 articles were included for review and analysis.RESULTS AND CONCLUSION:(1)The gut microbiota of elite athletes differs from that of the general population,characterized by increased α-diversity,elevated Firmicutes/Bacteroidetes ratio,increased abundance of beneficial bacteria,and enrichment of functional pathways contributing to athletic performance.(2)The type of sport and training load are closely related to the species structure and functional expression of the gut microbiota in athletes.(3)The bidirectional communication between the host and gut microbiota mediated by metabolites is an important mechanism by which exercise influences the gut microbiota.(4)Phase training typically induces adaptive changes in the gut microbiota,and alterations in the structure or function of the microbiota have lasting effects.(5)The type,quantity,and combination of macronutrients intake can significantly influence the structure and function of the gut microbiota,and interact synergistically or antagonistically with exercise training.(6)In the future,it is important to continue the exploration of the gut microbiota in athletes,clarify causal relationships,and establish new targets for exercise training interventions.

With the vigorous development of computers and internet, the construction of the information management system for Occupational Health Technical Service (OHTS) institutions in China has achieved impressive progress. But for the management of OHTS institutions, there are relatively few systems that can fully explore and utilize OHTS information. Base on this background, in light of the actual situation of the OHTS institution in Henan Province, an OHTS Information Management System was developed under the Java Spring Boot framework, with a MySQL database and a B/S multi-tier architecture. The platform integrates a vertical three-level network of ″provincial-municipal-county/district″ and a horizontal network involving health commissions, disease prevention and control bureaus, Centers for Disease Control and Prevention (occupational disease prevention and treatment institutes), and OHTS institutions. The system includes five core modules: dynamic management of institutional and personnel qualifications, full-process project supervision (including five categories of technical services such as pre-evaluation and control-effectiveness evaluation), multidimensional decision analysis (including eight statistical indicators of institutional distribution, equipment allocation, and occupational hazard factors), rapid generation and automated submission of various reports, and early warning and intelligent supervision. The system has been implemented in 61 OHTS institutions in Henan Province, improving the ″off-site supervision rate″ of supervision department and promoting the standardization and digital transformation of occupational health services.

BACKGROUND:Understanding the characteristics and influencing factors of the gut microbiota in athletes can help determine the optimal gut microbial composition for relevant sport events.Further investigation in this area could provide important insights for improving athletic performance and recovery as well as developing personalized nutrition prescriptions.OBJECTIVE:To summarize the characteristics of gut microbiota in athletes,and to elucidate the important factors influencing the gut microbiota characteristics of athletes from the perspectives of exercise training and dietary intake.METHODS:A literature search was conducted using the PubMed,ScienceDirect,CNKI,WanFang and VIP databases for publications from 2004 to 2024.The search terms included"microbiota,microbiome,athlete,exercise,training,diet,nutrition,dietary fiber,protein,ketogenic,fat"in English and Chinese.After excluding studies of poor quality and irrelevant content,a total of 65 articles were included for review and analysis.RESULTS AND CONCLUSION:(1)The gut microbiota of elite athletes differs from that of the general population,characterized by increased α-diversity,elevated Firmicutes/Bacteroidetes ratio,increased abundance of beneficial bacteria,and enrichment of functional pathways contributing to athletic performance.(2)The type of sport and training load are closely related to the species structure and functional expression of the gut microbiota in athletes.(3)The bidirectional communication between the host and gut microbiota mediated by metabolites is an important mechanism by which exercise influences the gut microbiota.(4)Phase training typically induces adaptive changes in the gut microbiota,and alterations in the structure or function of the microbiota have lasting effects.(5)The type,quantity,and combination of macronutrients intake can significantly influence the structure and function of the gut microbiota,and interact synergistically or antagonistically with exercise training.(6)In the future,it is important to continue the exploration of the gut microbiota in athletes,clarify causal relationships,and establish new targets for exercise training interventions.

Objective To investigate the clinical effect of cranial electrotherapy stimulation （CES） combined with music intervention on insomnia and its effect in improving cognitive function. Methods A total of 75 patients with insomnia were randomly divided into control group with treatment-as-usual （TAU） alone，CES+double beat+TAU group， and CES+pink noise+TAU group， with a sample size of 1∶1∶1 for the three groups. Sleep onset latency （SL）， total sleep time （TST）， and sleep quality were analyzed before and after treatment， and the Go/NoGo paradigm for the assessment of cognitive function was used to assess the effect of CES combined with music intervention in improving cognitive function. Results As for the treatment of insomnia， the experimental groups had significant changes in sleep quality （PSQI and SRSS） （P<0.05） and a significant reduction in sleep latency （P<0.05）after treatment， with a significantly higher reduction rate than the control group. As for cognitive function， there was a reduction in the latency of Go-N2 and Go-P3 and an increase in amplitude in the patients with insomnia after CES combined with music intervention， as well as reductions in the latency and amplitude of NoGo-N2 and NoGo-P3. Conclusion CES combined with music intervention can safely and effectively shorten sleep latency and improve sleep quality and cognitive function.

Objective:To investigate the influencing factors of enlarged perivascular space (PVS) in relapsing-remitting multiple sclerosis (RRMS) patients and their relationship with cognitive function.Methods:Twenty-seven individuals with RRMS (RRMS group) and 27 healthy controls (healthy control group) who presented to the Department of Neurology, the Sixth Medical Center of People′s Liberation Army General Hospital from July 2022 to November 2024 underwent cognitive function assessments. PVS volume fractions, lesion volumes, and brain volumes were calculated using FreeSurfer, FSL, and other relevant softwares. Group differences in PVS volume fractions, lesion volumes, brain volumes, and cognitive function assessments were compared. Furthermore, correlations between PVS volume fractions and lesion volumes, brain volumes, and cognitive function assessments were analyzed within the RRMS group.Results:Compared with the healthy control group, the RRMS group exhibited significantly higher PVS volume fractions in white matter (PVS_w) (3.14‰±0.29‰ vs 2.91‰±0.30‰, t=2.877, P=0.006) and PVS volume fractions in deep gray matter (PVS_d) (2.25‰±0.10‰ vs 2.17‰±0.09‰, t=2.681, P=0.010), indicating an enlargement of the PVS. Compared with the healthy control group, the RRMS group showed a significant decrease in both white matter volumes [297.3 (274.3, 340.2) ml vs (324.2 (311.0, 350.0) ml, U=-2.085, P=0.037] and deep grey matter volumes [40.2 (34.9, 43.6) ml vs 42.7 (40.2, 44.8) ml, U=-2.292, P=0.022]. Compared with the healthy control group, the RRMS group showed significantly lower scores in cognitive function assessments ( P<0.05). Univariate analysis showed that PVS_w in the RRMS group was significantly positively correlated with age ( r=0.486), white matter lesion volumes ( r=0.437) and deep gray matter lesion volumes ( r=0.394;all P<0.05); PVS_d was also significantly positively correlated with white matter lesion volumes ( r=0.418) and deep gray matter lesion volumes ( r=0.480; both P<0.05). Multiple linear regression analysis showed that age ( B=0.011,95% CI 0.004-0.017), white matter lesion volumes ( B=0.026,95% CI 0.011-0.040) and deep gray matter lesion volumes ( B=0.401,95% CI 0.032-0.771) in the RRMS group were significantly positively correlated with PVS_w, while white matter lesion volumes ( B=0.007,95% CI 0.001-0.014) and deep gray matter lesion volumes ( B=0.204,95% CI 0.029-0.380) were significantly positively correlated with PVS_d (both P<0.05). Univariate analysis showed that immediate memory score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.428), and was significantly positively correlated with education level ( r=0.471), deep gray matter volumes ( r=0.530) and total brain volumes ( r=0.389; all P<0.05); short-term delayed memory score in the RRMS group was significantly negatively correlated with age ( r=-0.390), PVS_w ( r=-0.417) and white matter lesion volumes ( r=-0.438), and was significantly positively correlated with gender ( r=0.393), white matter volumes ( r=0.478), deep gray matter volumes ( r=0.579) and total brain volumes ( r=0.602;all P<0.05); verbal fluency test score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.409) and was significantly positively correlated with education level ( r=0.419) and total brain volumes ( r=0.400;all P<0.05). Multiple linear regression analysis revealed that PVS_d ( B=-5.572, 95% CI -11.513--0.368) and brain volumes ( B=0.012, 95% CI 0.001-0.023) in the RRMS group were both significant predictors of immediate recall score, while PVS_d ( B=-14.203,95% CI -27.514--0.891) was an independent predictor of verbal fluency test score (all P<0.05). Conclusions:The PVS is enlarged in individuals with RRMS compared with the healthy controls, and increased lesion volumes may be a significant predictor. Furthermore, enlarged PVS in the deep gray matter may be a significant predictor of impairment of verbal memory and verbal function in individuals with RRMS.

Objective:To investigate the influencing factors of enlarged perivascular space (PVS) in relapsing-remitting multiple sclerosis (RRMS) patients and their relationship with cognitive function.Methods:Twenty-seven individuals with RRMS (RRMS group) and 27 healthy controls (healthy control group) who presented to the Department of Neurology, the Sixth Medical Center of People′s Liberation Army General Hospital from July 2022 to November 2024 underwent cognitive function assessments. PVS volume fractions, lesion volumes, and brain volumes were calculated using FreeSurfer, FSL, and other relevant softwares. Group differences in PVS volume fractions, lesion volumes, brain volumes, and cognitive function assessments were compared. Furthermore, correlations between PVS volume fractions and lesion volumes, brain volumes, and cognitive function assessments were analyzed within the RRMS group.Results:Compared with the healthy control group, the RRMS group exhibited significantly higher PVS volume fractions in white matter (PVS_w) (3.14‰±0.29‰ vs 2.91‰±0.30‰, t=2.877, P=0.006) and PVS volume fractions in deep gray matter (PVS_d) (2.25‰±0.10‰ vs 2.17‰±0.09‰, t=2.681, P=0.010), indicating an enlargement of the PVS. Compared with the healthy control group, the RRMS group showed a significant decrease in both white matter volumes [297.3 (274.3, 340.2) ml vs (324.2 (311.0, 350.0) ml, U=-2.085, P=0.037] and deep grey matter volumes [40.2 (34.9, 43.6) ml vs 42.7 (40.2, 44.8) ml, U=-2.292, P=0.022]. Compared with the healthy control group, the RRMS group showed significantly lower scores in cognitive function assessments ( P<0.05). Univariate analysis showed that PVS_w in the RRMS group was significantly positively correlated with age ( r=0.486), white matter lesion volumes ( r=0.437) and deep gray matter lesion volumes ( r=0.394;all P<0.05); PVS_d was also significantly positively correlated with white matter lesion volumes ( r=0.418) and deep gray matter lesion volumes ( r=0.480; both P<0.05). Multiple linear regression analysis showed that age ( B=0.011,95% CI 0.004-0.017), white matter lesion volumes ( B=0.026,95% CI 0.011-0.040) and deep gray matter lesion volumes ( B=0.401,95% CI 0.032-0.771) in the RRMS group were significantly positively correlated with PVS_w, while white matter lesion volumes ( B=0.007,95% CI 0.001-0.014) and deep gray matter lesion volumes ( B=0.204,95% CI 0.029-0.380) were significantly positively correlated with PVS_d (both P<0.05). Univariate analysis showed that immediate memory score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.428), and was significantly positively correlated with education level ( r=0.471), deep gray matter volumes ( r=0.530) and total brain volumes ( r=0.389; all P<0.05); short-term delayed memory score in the RRMS group was significantly negatively correlated with age ( r=-0.390), PVS_w ( r=-0.417) and white matter lesion volumes ( r=-0.438), and was significantly positively correlated with gender ( r=0.393), white matter volumes ( r=0.478), deep gray matter volumes ( r=0.579) and total brain volumes ( r=0.602;all P<0.05); verbal fluency test score in the RRMS group was significantly negatively correlated with PVS_d ( r=-0.409) and was significantly positively correlated with education level ( r=0.419) and total brain volumes ( r=0.400;all P<0.05). Multiple linear regression analysis revealed that PVS_d ( B=-5.572, 95% CI -11.513--0.368) and brain volumes ( B=0.012, 95% CI 0.001-0.023) in the RRMS group were both significant predictors of immediate recall score, while PVS_d ( B=-14.203,95% CI -27.514--0.891) was an independent predictor of verbal fluency test score (all P<0.05). Conclusions:The PVS is enlarged in individuals with RRMS compared with the healthy controls, and increased lesion volumes may be a significant predictor. Furthermore, enlarged PVS in the deep gray matter may be a significant predictor of impairment of verbal memory and verbal function in individuals with RRMS.

Objective:To investigate the differences and applicability of free silica detection methods of different crystal forms in dust, and to provide a basis for the selection of various methods.Methods:From December 2021 to June 2022, dust samples from 20 enterprises in different industries in 18 cities in Henan Province were randomly selected as the investigation objects. X-ray diffraction (XRD) method was used to analyze the samples and classify the samples. Based on GBZ/T 192.4-2007 "Determination of Dust in the Air of Workplace-Part 4: Content of Free Silica in Dust", pyrophosphate method and infrared spectrophotometry were used for quantitative determination. The measured results were analyzed by paired sample t test to evaluate the advantages and disadvantages of the two methods and their applicable scope. Results:The XRD results of 20 dust samples could be divided into α, β, γ crystal types and the mixed type of α and γ. There was no significant difference between pyrophosphate method and infrared spectrophotometry ( P=0.180). The pyrophosphate method results of β, γ and α, γ mixed crystalline free silica were significantly higher than those of infrared spectrophotometry, and the difference was statistically significant ( P<0.001) . Conclusion:Pyrophosphate method and infrared spectrophotometry are suitable for α-type free silica, while pyrophosphate method is suitable for β, γ and α, γ mixed crystalline free silica.

Objective:To investigate the etiology and prognosis of severe acute respiratory infection (SARI) in <5 year old outpatients.Methods:The data of 199 children with SARI admitted to the outpatient department of the Xi′an Children′s Hospital from January 2022 to December 2023 were included. Nasopharyngeal swabs were collected and the etiology of SARI virus in children was determined by polymerase chain reaction (PCR) within 48 h of study inclusion. The factors influencing respiratory support and length of stay in SARI children were analyzed by multiple linear regression.Results:Among the 199 patients included, 183 patients had positive nasopharyngeal swabs, with a positive rate of 92.0%. One pathogen was found in 124(62.3%) positive patients, two pathogens were found in 53(26.6%) positive patients, and three pathogens were found in 6(3.0%) positive patients. The most commonly detected viruses were rhinovirus (42.7%, 85/199), respiratory syncytial virus (33.2%, 66/199) and parainfluenza virus (14.1%, 28/199). Respiratory syncytial virus was detected in 40(54.8%) of the samples collected from infants aged 0 to 6 months, significantly higher than the frequency found in other age groups ( P<0.05). Adenoviruses were more common in the >12-24 month age group (22.0%). Age-specific weight Z score ( β=-0.223, 95% CI=-1.042--0.136, P=0.011) and SpO 2 ( β=-0.237, 95% CI=-0.21--0.037, P=0.006) were the influencing factors for respiratory support days in children with SARI. Age-specific weight Z score ( β=-0.223, 95% CI=-1.049--0.124, P=0.013) and SpO 2 ( β=-0.209, 95% CI=-0.204--0.020, P=0.017) were the factors influencing the length of hospital stay in SARI children. Conclusions:The findings of this study highlight the importance of viruses as SARI-associated pathogens in this population. Children at risk of a severe course of disease can be identified by measuring their age-specific weight Z score and SpO 2 on admission.

Objective: To systematically evaluate the influencing factors for medication compliance in patients with comorbidities of chronic diseases, so as to provide the evidence for improving medication compliance. Methods: Literature on influencing factors for medication compliance in patients with comorbidities of chronic diseases were retrived from CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, Cochrane Library and Embase from inception to January 20, 2024. After independent literature screening, data extraction, and quality assessment by two researchers, a meta-analysis was performed using RevMan 5.4 and Stata 16.0 softwares. Literature were excluded one by one for sensitivity analysis. Publication bias was assessed using Egger's test. Results: Initially, 7 365 relevant articles were retrieved, and 35 of them were finally included, with a total sample size of about 150 000 individuals. There were 30 cross-sectional studies and 5 cohort studies; and 11 high-quality studies and 24 medium-quality studies. The meta-analysis showed that the demographic factors of lower level of education (OR=2.148, 95%CI: 1.711-2.696), lower economic income (OR=1.897, 95%CI: 1.589-2.264), male (OR=0.877, 95%CI: 0.782-0.985), living alone (OR=2.833, 95%CI: 1.756-4.569) and unmarried (OR=2.784, 95%CI: 1.251-6.196); the medication treatment factors of polypharmacy (OR=1.794, 95%CI: 1.190-2.706), potentially inappropriate medication (OR=2.988, 95%CI: 1.527-5.847), low frequency of daily medication (OR=0.533, 95%CI: 0.376-0.754) and adverse drug reactions (OR=3.319, 95%CI: 1.967-5.602); the disease factors of long course of disease (OR=2.118, 95%CI: 1.643-2.730), more comorbidities (OR=1.667, 95%CI: 1.143-2.431) and cognitive impairment (OR=2.007, 95%CI: 1.401-2.874); and the psychosocial factors of poor belief in taking medication (OR=1.251, 95%CI: 1.011-1.547), poor self-rated health (OR=1.990, 95%CI: 1.571-2.522) and being guided by healthcare professionals (OR=0.151, 95%CI: 0.062-0.368) were the influencing factors for medication compliance in patients with chronic comorbidities. Conclusion The medication compliance in patients with comorbidities of chronic diseases is associated with demographic factors, pharmacological factors, disease factors and psychosocial factors, mainly including living alone, adverse drug reactions, course of disease, number of comorbidities and medication beliefs.

Objective:To investigate the differences and applicability of free silica detection methods of different crystal forms in dust, and to provide a basis for the selection of various methods.Methods:From December 2021 to June 2022, dust samples from 20 enterprises in different industries in 18 cities in Henan Province were randomly selected as the investigation objects. X-ray diffraction (XRD) method was used to analyze the samples and classify the samples. Based on GBZ/T 192.4-2007 "Determination of Dust in the Air of Workplace-Part 4: Content of Free Silica in Dust", pyrophosphate method and infrared spectrophotometry were used for quantitative determination. The measured results were analyzed by paired sample t test to evaluate the advantages and disadvantages of the two methods and their applicable scope. Results:The XRD results of 20 dust samples could be divided into α, β, γ crystal types and the mixed type of α and γ. There was no significant difference between pyrophosphate method and infrared spectrophotometry ( P=0.180). The pyrophosphate method results of β, γ and α, γ mixed crystalline free silica were significantly higher than those of infrared spectrophotometry, and the difference was statistically significant ( P<0.001) . Conclusion:Pyrophosphate method and infrared spectrophotometry are suitable for α-type free silica, while pyrophosphate method is suitable for β, γ and α, γ mixed crystalline free silica.