Background Air pollution has widespread adverse health effects on human health, but research on its impact on reproductive health and related physiological mechanisms remains insufficient. Objective To investigate the associations between short-term exposure to particulate matter and nitrogen dioxide (NO₂) and serum levels of anti-Müllerian hormone (AMH) and testosterone in healthy adults. Methods A total of 152 healthy adults were recruited for a panel study, undergoing four follow-up visits from September 2019 to January 2020. Basic demographic information and serum samples were collected. Reproductive hormone levels were measured by enzyme-linked immunosorbent assay kits. Concurrently, environmental data in the study area were monitored, including fine particulate matter (PM_2.5), black carbon (BC), NO₂, particle number concentrations in the 5-560 nm size fraction (PNC_{5_560}), and meteorological variables. Linear mixed-effects models were constructed to estimate the effects of short-term exposure to these particulate matter on serum reproductive hormone levels. Results The participants had an average age of (23.9±2.4) years, with 102 women. For each interquartile range (IQR) increment in pollutant moving average concentrations, PM_2.5, PNC_{5_25}, PNC_{100_560}, BC, and NO₂ were associated with relative decreases in female serum AMH levels by 10.2%-16.9%, 46.2%-58.0%, 16.9%-21.2%, 14.8%-26.5%, and 11.3%-17.6%, respectively. These negative associations were stronger in females with waist-to-hip ratio > 0.76 and physical activity levels ≤ 1217 metabolic equivalents (MET) min per week. Similarly, in males, an IQR increase in the same pollutants was associated with relative decreases in serum free testosterone by 11.1%-17.2%, 35.2%, 10.5%-18.9%, 18.7%-24.2%, and 10.3%-26.0%, respectively. These effects were more pronounce in male with a waist-to-hip ratios > 0.81 and physical activity levels ≤ 1498 MET-min per week. Conclusion Short-term exposure to particulate matter and NO₂ may reduce serum levels of AMH and testosterone in healthy adults. Furthermore, these adverse effects appears to be stronger in adults with higher waist-to-hip ratios.

Objective:To analyze the screening results of the Urban Cancer Early Diagnosis and Treatment Project in Qinghai Province from 2019 to 2024.Methods:A summary and statistical analysis were conducted on six years of screening data from the Urban Cancer Early Dia-gnosis and Treatment Program in Qinghai Province,with the high-risk rate,screening rate,and detection rate calculated separately for each type of cancer.Results:From 2019 to 2024,56,882 high-risk individuals were identified.The high-risk rates for lung,colorectal,breast,up-per gastrointestinal,and liver cancer were 22.02%,21.57%,14.23%,13.52%,and 6.10%,respectively.Overall,13,592 individuals com-pleted clinical screening,with detection rates of 0.32%for lung cancer,0.41%for liver cancer,0.08%for precancerous gastric lesions,3.63%for precancerous colorectal lesions,0.08%for esophageal cancer,0.16%for gastric cancer,and 0.14%for colorectal cancer.Conclusions:The implementation of the Urban Cancer Early Diagnosis and Treatment Program in Qinghai Province aids in the early detection of cancer,improves early diagnosis and survival rates,and reduces mortality.Nevertheless,due to low public awareness and limited participation,en-hancements in program management and public outreach are required.

Stroke-associated pneumonia(SAP),a frequent complication of stroke,adversely affects clinical outcomes and functional recovery.Identifying SAP risk factors and developing robust predictive models are critical for improving patient management.This article reviews recent research advances in SAP risk factors and risk prediction,emphasizes emerging risk factors-including sarcopenia epidemiology,gut microbiota dysbiosis,and thyroid dysfunction-and novel predictive approaches such as risk stratification scores,neuroimaging,biomarkers,and artificial intelligence.We aim to enhance clinical recognition of SAP to facilitate early intervention,reduce incidence,and optimize stroke prognosis.

Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To identify the hemodynamic characteristics of frontal polar cortex(FPC)in patients with chronic subjective tinnitus,and to study the short-term efficacy of multielement integration sound(MIS)treatment,and its effects on FPC oxyhemoglobin(HbO).Methods Fifty patients with chronic subjective tinnitus(tinnitus group)and 50 subjects without tinnitus matching their age,sex and education level(control group)were collected from June 2023 to Oc-tober 2023.The tinnitus group and control group received MIS treatment for 15 minutes,respectively.Tinnitus handicap inventory(THI)and visual analogue scale(VAS)scores were collected before and after treatment in tinnitus group.Func-tional near infrared spectroscopy(fNIRS)was used to measure the 8-minute average HbO concentration in the frontal cortex of both groups before and after treatment.The changes of HbO concentration before and after treatment were compared be-tween the two groups.The correlation between clinical features and HbO was analyzed.Results The VAS score of the tin-nitus group decreased after short-term MIS treatment.The HbO concentration of FPC in tinnitus group was higher than that in control group before treatment.The HbO concentration of FPC in tinnitus group was decreased by MIS short-term treatment.The difference of HbO concentration before and after treatment(ΔHbO)was positively correlated with the difference of VAS score before and after treatment(ΔVAS)in the tinnitus group.Conclusion The hemodynamics of the frontal polar cortex in chronic subjective tinnitus patients is different from that of in non-tinnitus control group.MIS can change the hemodynamics of the frontal polar cortex in chronic subjective tinnitus patients.The frontal polar cortex may be the site of MIS.

Stroke-associated pneumonia(SAP),a frequent complication of stroke,adversely affects clinical outcomes and functional recovery.Identifying SAP risk factors and developing robust predictive models are critical for improving patient management.This article reviews recent research advances in SAP risk factors and risk prediction,emphasizes emerging risk factors-including sarcopenia epidemiology,gut microbiota dysbiosis,and thyroid dysfunction-and novel predictive approaches such as risk stratification scores,neuroimaging,biomarkers,and artificial intelligence.We aim to enhance clinical recognition of SAP to facilitate early intervention,reduce incidence,and optimize stroke prognosis.