ObjectiveTo analyze the infection status of main respiratory pathogens in influenza-like illness (ILI) cases in Anji County, Huzhou City, Zhejiang Province, and to provide a reference for the prevention, diagnosis, and treatment of respiratory infections. MethodsThroat swab samples were collected from 520 ILI cases in an influenza sentinel surveillance hospital in Anji County of Zhejiang Province from December 2023 to November 2024. Multiplex real-time fluorescence quantitative polymerase chain reaction (mRT-PCR) was used to detect 18 pathogens and their subtypes, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (Flu A), influenza A (H1N1) virus, influenza A (H3N2) virus, influenza B virus (Flu B), influenza B virus Victoria lineage (BV), influenza B virus Yamagata lineage (BY), coronavirus (CoV), human parainfluenza virus (HPIV), respiratory syncytial virus (RSV), human metapneumovirus (HMPV), adenovirus (ADV), human bocavirus (HBoV), enterovirus (EV), rhinovirus (RV), Mycoplasma pneumoniae (MP), Chlamydia pneumoniae (CP), and Streptococcus pneumoniae (SP). ResultsThe overall positivity rate of pathogens in 520 samples was 33.65%, among which the detection rates of Flu (9.14%), ADV (7.50%), SARS-CoV-2 (6.15%), and EV (3.65%) were relatively high. There were statistically significant differences in the overall positivity rate of pathogens by age and season (all P<0.05). The highest overall positivity rate was observed in the 5‒14 years old group (42.77%), and the overall positivity rate in winter (53.08%) was significantly higher than that in other seasons. ConclusionFrom 2023 to 2024, the main respiratory pathogens detected in ILI cases in Anji County were Flu, ADV, SARS-CoV-2, and EV. The epidemic characteristics showed age and seasonal specificity, so it is necessary to strengthen prevention and control for high-risk populations and epidemic seasons in a targeted manner.

Objective:To characterize the vaginal flora of pregnant women during the second trimester using full-length 16S rRNA sequencing.Methods:A total of 142 pregnant women were systematically sampled from a pregnancy cohort. Vaginal swabs were collected for full-length 16S rRNA gene sequencing，and bioinformatics analysis was performed to characterize the vaginal microbiota and identify associated influencing factors.Results:Among the 142 pregnant women，the most frequently detected species were Lactobacillus iners（83.10%，118/142）and Lactobacillus crispatus（49.30%，70/142）. The majority of samples（90.85%，129/142）were classified as Lactobacillus-dominant vagitypes，with the Lactobacillus iners vagitype accounting for 48.59%（69/142）and the Lactobacillus crispatus vagitype accounting for 38.73%（55/142）. The vaginal microbiota was clustered into five community state types（CSTs）：Ⅰa，Ⅰb，Ⅲa，Ⅲb，and Ⅳ. The most prevalent CSTs were Lactobacillus iners-dominated CST-Ⅲ（51.41%，73/142）and Lactobacillus crispatus-dominated CST-Ⅰ（24.65%，35/142）. No samples were classified as CST-Ⅱ or CST-Ⅴ. A significant negative correlation was observed between Lactobacill and vaginosis-associated bacteria. Age，alcohol consumption，smoking，and vaginal treatments showed significant associations or trends toward significance with various Alpha diversity indices. Vaginal douching was associated with CST clustering，while obstetric history（primiparity，previous miscarriage history）was associated with vagitype classification. However，no significant associations were identified between maternal baseline characteristics and Beta diversity indices. Conclusions:Full-length 16S rRNA gene sequencing reveals that the vaginal microbiota of pregnant women is dominated by Lactobacillus iners and Lactobacillus crispatus. Maternal age，lifestyle factors such as smoking and alcohol consumption，and obstetric history are significantly associated with variations in vaginal microbiota composition.

Objective:To analyze the epidemiological characteristics and infection sources of cholera in China from 2005 to 2024.Methods:A total of 2 066 cholera cases were included in the study, which were obtained from the China Disease Control and Prevention Information System (CDPCIS) of China CDC. The information on cholera clusters was downloaded from the National Public Health Emergency Event Surveillance System (PHEESS) of China CDC. A total of 128 cholera clusters were included and analyzed in this study. The epidemiological characteristics and infection sources of cholera were analyzed. The Jointpoint model was applied to analyze the incidence trend, and annual percentage change (APC) was also quantified.Results:From 2005 to 2024, a total of 2 066 cholera cases were reported, with an average of 103 cases reported annually. Specifically, the incidence showed a marked downward trend from 2004 to 2015 ( APC=-26.78%, P=0.006). During 2015-2024, the disease remained at low endemic levels, with an average of 18 reported cases annually ( APC=-2.68%, P=0.807). Cholera peak season was from May to October. A total of 24 provinces reported cholera cases, which were mainly distributed in Zhejiang, Fujian, Beijing, Jiangsu, Anhui, Guangdong, and Hainan provinces, accounting for 78.03% of the total cases. Pathogen surveillance indicated an alternating prevalence of Vibrio cholerae serogroups O1 and O139 among laboratory-confirmed cases between 2005 and 2024. There was a disparity in the dominant serogroup of Vibrio cholerae by region. The results from 128 cholera clusters indicated that cholera outbreaks frequently occurred in rural banquets (64.84%), followed by regular restaurants (13.28%). Among these, 63 clusters (49.22%) with identified infection sources indicated that foodborne transmission (95.24%) was the primary mode of cholera transmission, which mainly through seafood and aquatic products, such as soft-shelled turtles, shrimp and shellfish. The characteristics of cholera clusters caused by Vibrio cholerae serogroups O1 and O139 showed statistically significant differences in scale, attack rate, place of residence, setting, and infection source ( P<0.05). Conclusion:Cholera incidence has remained consistently low since 2015 in China, mainly in sporadic cases. Rural gatherings (e.g., wedding banquets) are the main settings for cholera clusters. The main infection sources are predominantly caused by cross-contamination due to improper processing practices of aquatic products, such as soft-shelled turtles.

Objective:To investigate the impact of early invasive arterial blood pressure (IBP) monitoring on survival and neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR).Methods:This retrospective cohort study analyzed 44 OHCA patients receiving ECPR between January 2021 and January 2023. Patients were divided into: Early intervention group : IBP established within 3 min of ECMO initiation; Late intervention group : IBP established after ICU admission. Baseline characteristics, ECMO parameters, and clinical outcomes were compared. Multivariable logistic regression (adjusted for age, initial rhythm, etc.) and Spearman's correlation were used.Results:This study included a total of 44 patients treated with OHCA and ECPR, divided into an early intervention group of 23 cases and a late intervention group of 21 cases. The early intervention group showed significantly higher: Survival to discharge (43.5% vs. 9.5%, P<0.05), Good neurological recovery (CPC 1-2: 34.8% vs. 9.5%, P<0.05).Early intervention independently predicted survival (adjusted OR=18.84, 95% CI:1.97-179.98, P=0.01). Stratified analysis by pH (cutoff 7.0) demonstrated consistent benefits in both pH>7.0 ( aOR=0.392, 95% CI:0.106-0.678) and pH≤7.0 subgroups ( aOR=0.385, 95% CI: 0.075-0.695; interaction P=0.183). Early IBP positively correlated with CPC scores ( ρ=0.40, P=0.007). Conclusions:Early IBP monitoring significantly improves survival and neurological outcomes in OHCA-ECPR patients, supporting its integration into standardized protocols.

Objective:To investigate the diversity, temporal dynamics and influencing factors of serotypes of Salmonella among asymptomatic workers in Yulin, Guangxi Zhuang Autonomous Region, from 2013 to 2023, and provide reference for controlling the hidden transmission of Salmonella and for disease prevention among healthy populations. Methods:Anal rectal swabs were collected from asymptomatic workers in Yulin from 2013 to 2023 for the isolation and identification of Salmonella. The diversity of serotypes of Salmonella was evaluated by using Shannon index, while the isolation rates and composition ratio of different serotypes were compared with χ2 test. Spearman correlation analysis was used to evaluate the correlation of natural and social factors with serotype isolation rates for more than eight years, and stepwise multiple linear regression analysis were used to evaluate the impact of social factors on the serotype isolation rates for more than 8 years. Results:A total of 8 073 strains of Salmonella were isolated from 2013 to 2023, with an overall isolation rate of 18.88‰ During the 11-year period, 135 serotypes of Salmonella were identified, and the serotype diversity increased first and then decreased. The serotype with the highest isolation rate was Salmonella Typhimurium (3.04‰), followed by Salmonella Rissen (1.70‰), Salmonella Corvallis (1.41‰), Salmonella Agona (1.36‰), Salmonella Derby (1.22‰), and Salmonella London (1.02‰), accounting for 51.67% of the total Salmonella serotypes isolated. There was no significant trend in isolation rate of Salmonella over the years ( χ2=0.67, P=0.415); however, season significantly influenced the isolation rate, with lower rate being observed in winter and higher rate in summer ( χ2=615.91, P<0.001). The isolation rate of different serotypes of Salmonella was influenced by social and natural factors, showing a positive correlation with temperature, precipitation, and poultry consumption and production levels. Conclusions:The serotypes of Salmonella carried by asymptomatic workers were complex and diverse in Yulin, Guangxi Zhuang Autonomous Region, from 2013 to 2023, and the isolation rates of multi-serotypes increased from 2013 to 2023. Higher isolation rate was observed in summer and the isolation rate was widely influenced by natural factors such as temperature and precipitation, as well as social factors such as economic conditions. It is necessary to pay attention to the Salmonella serotype carriage in healthy population, and strengthen monitoring of both healthy individuals and pathogenic bacteria.

Objective:To analyze the epidemiological characteristics and infection sources of cholera in China from 2005 to 2024.Methods:A total of 2 066 cholera cases were included in the study, which were obtained from the China Disease Control and Prevention Information System (CDPCIS) of China CDC. The information on cholera clusters was downloaded from the National Public Health Emergency Event Surveillance System (PHEESS) of China CDC. A total of 128 cholera clusters were included and analyzed in this study. The epidemiological characteristics and infection sources of cholera were analyzed. The Jointpoint model was applied to analyze the incidence trend, and annual percentage change (APC) was also quantified.Results:From 2005 to 2024, a total of 2 066 cholera cases were reported, with an average of 103 cases reported annually. Specifically, the incidence showed a marked downward trend from 2004 to 2015 ( APC=-26.78%, P=0.006). During 2015-2024, the disease remained at low endemic levels, with an average of 18 reported cases annually ( APC=-2.68%, P=0.807). Cholera peak season was from May to October. A total of 24 provinces reported cholera cases, which were mainly distributed in Zhejiang, Fujian, Beijing, Jiangsu, Anhui, Guangdong, and Hainan provinces, accounting for 78.03% of the total cases. Pathogen surveillance indicated an alternating prevalence of Vibrio cholerae serogroups O1 and O139 among laboratory-confirmed cases between 2005 and 2024. There was a disparity in the dominant serogroup of Vibrio cholerae by region. The results from 128 cholera clusters indicated that cholera outbreaks frequently occurred in rural banquets (64.84%), followed by regular restaurants (13.28%). Among these, 63 clusters (49.22%) with identified infection sources indicated that foodborne transmission (95.24%) was the primary mode of cholera transmission, which mainly through seafood and aquatic products, such as soft-shelled turtles, shrimp and shellfish. The characteristics of cholera clusters caused by Vibrio cholerae serogroups O1 and O139 showed statistically significant differences in scale, attack rate, place of residence, setting, and infection source ( P<0.05). Conclusion:Cholera incidence has remained consistently low since 2015 in China, mainly in sporadic cases. Rural gatherings (e.g., wedding banquets) are the main settings for cholera clusters. The main infection sources are predominantly caused by cross-contamination due to improper processing practices of aquatic products, such as soft-shelled turtles.

Objective:To investigate the diversity, temporal dynamics and influencing factors of serotypes of Salmonella among asymptomatic workers in Yulin, Guangxi Zhuang Autonomous Region, from 2013 to 2023, and provide reference for controlling the hidden transmission of Salmonella and for disease prevention among healthy populations. Methods:Anal rectal swabs were collected from asymptomatic workers in Yulin from 2013 to 2023 for the isolation and identification of Salmonella. The diversity of serotypes of Salmonella was evaluated by using Shannon index, while the isolation rates and composition ratio of different serotypes were compared with χ2 test. Spearman correlation analysis was used to evaluate the correlation of natural and social factors with serotype isolation rates for more than eight years, and stepwise multiple linear regression analysis were used to evaluate the impact of social factors on the serotype isolation rates for more than 8 years. Results:A total of 8 073 strains of Salmonella were isolated from 2013 to 2023, with an overall isolation rate of 18.88‰ During the 11-year period, 135 serotypes of Salmonella were identified, and the serotype diversity increased first and then decreased. The serotype with the highest isolation rate was Salmonella Typhimurium (3.04‰), followed by Salmonella Rissen (1.70‰), Salmonella Corvallis (1.41‰), Salmonella Agona (1.36‰), Salmonella Derby (1.22‰), and Salmonella London (1.02‰), accounting for 51.67% of the total Salmonella serotypes isolated. There was no significant trend in isolation rate of Salmonella over the years ( χ2=0.67, P=0.415); however, season significantly influenced the isolation rate, with lower rate being observed in winter and higher rate in summer ( χ2=615.91, P<0.001). The isolation rate of different serotypes of Salmonella was influenced by social and natural factors, showing a positive correlation with temperature, precipitation, and poultry consumption and production levels. Conclusions:The serotypes of Salmonella carried by asymptomatic workers were complex and diverse in Yulin, Guangxi Zhuang Autonomous Region, from 2013 to 2023, and the isolation rates of multi-serotypes increased from 2013 to 2023. Higher isolation rate was observed in summer and the isolation rate was widely influenced by natural factors such as temperature and precipitation, as well as social factors such as economic conditions. It is necessary to pay attention to the Salmonella serotype carriage in healthy population, and strengthen monitoring of both healthy individuals and pathogenic bacteria.

Objective:To characterize the vaginal flora of pregnant women during the second trimester using full-length 16S rRNA sequencing.Methods:A total of 142 pregnant women were systematically sampled from a pregnancy cohort. Vaginal swabs were collected for full-length 16S rRNA gene sequencing，and bioinformatics analysis was performed to characterize the vaginal microbiota and identify associated influencing factors.Results:Among the 142 pregnant women，the most frequently detected species were Lactobacillus iners（83.10%，118/142）and Lactobacillus crispatus（49.30%，70/142）. The majority of samples（90.85%，129/142）were classified as Lactobacillus-dominant vagitypes，with the Lactobacillus iners vagitype accounting for 48.59%（69/142）and the Lactobacillus crispatus vagitype accounting for 38.73%（55/142）. The vaginal microbiota was clustered into five community state types（CSTs）：Ⅰa，Ⅰb，Ⅲa，Ⅲb，and Ⅳ. The most prevalent CSTs were Lactobacillus iners-dominated CST-Ⅲ（51.41%，73/142）and Lactobacillus crispatus-dominated CST-Ⅰ（24.65%，35/142）. No samples were classified as CST-Ⅱ or CST-Ⅴ. A significant negative correlation was observed between Lactobacill and vaginosis-associated bacteria. Age，alcohol consumption，smoking，and vaginal treatments showed significant associations or trends toward significance with various Alpha diversity indices. Vaginal douching was associated with CST clustering，while obstetric history（primiparity，previous miscarriage history）was associated with vagitype classification. However，no significant associations were identified between maternal baseline characteristics and Beta diversity indices. Conclusions:Full-length 16S rRNA gene sequencing reveals that the vaginal microbiota of pregnant women is dominated by Lactobacillus iners and Lactobacillus crispatus. Maternal age，lifestyle factors such as smoking and alcohol consumption，and obstetric history are significantly associated with variations in vaginal microbiota composition.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.