Objective To explore the related risk factors of osteosarcopenia (OS) in elderly patients with type 2 diabetes mellitus (T2DM) and to evaluate their predictive value. Methods We selected 409 elderly patients with T2DM from our hospital between June 2021 and December 2024 as the study subjects, and divided them into an OS occurrence group and a non-occurrence group based on whether they were diagnosed with OS. Results Among the 409 elderly patients with T2DM included, 93 were diagnosed with OS, yielding a prevalence rate of 22.73%. Spearman correlation analysis revealed a significant association between lumbar spine BMD and T-scores with age, history of previous fractures, fasting plasma glucose (FPG), procollagen type I N-terminal propeptide (PINP), osteocalcin (OC), and 25-hydroxyvitamin D (25(OH)D). Gender (OR=0.193), Body Mass Index (BMI) (OR=0.254), history of previous fractures (OR=8.883), FPG (OR=0.543), Total Cholesterol (TC) (OR=3.684), High-Density Lipoprotein Cholesterol (HDL-C) (OR=86.024), PINP (OR=0.818), and OC (OR=0.526) are identified as influential factors for the occurrence of OS in elderly patients with T2DM. The combined prediction of these variables yields a sensitivity of 96.5%, a specificity of 97.8%, and an area under the curve (AUC) of 0.992 for the occurrence of OS in elderly patients with T2DM, indicating an excellent predictive performance. Conclusion The following factors—gender, BMI, history of previous fractures, FPG, TC, HDL-C, PINP, and OC—are influential in the occurrence of OS among elderly patients with T2DM. Formulating intervention measures based on these influencing factors can provide assistance in preventing and treating the occurrence of OS.

Background The contamination of public baths with Legionella pneumophila contamination has become a growing public health concern in recent years. However, research on its association with bacterial community characteristics in water samples remains limited. The integration of 16S rRNA sequencing and random forest modeling provides a new approach to elucidate the bacterial community characteristics of public bath water and their association with Legionella pneumophila contamination. Objective To investigate the bacterial community structure and diversity of public bath water in Shanghai, explore the association between Legionella pneumophila contamination and bacterial community characteristics, and identify key bacterial genera associated with contamination, thereby providing a scientific basis for formulating hygiene management regulations for public bath water. Methods From February to March 2023, water samples were collected from ten public baths in Shanghai which were selected based on business scale, regional distribution, and functional differences. Water quality parameters were evaluated, and the samples were categorized into Legionella-positive and Legionella-negative groups based on the detection results of Legionella pneumophila. The bacterial community structure, α-diversity, and β-diversity were analyzed using 16S rRNA sequencing. Redundancy analysis (RDA) was employed to examine the relationship between physicochemical factors and bacterial community diversity. A random forest model was employed to identify key bacterial genera distinguishing the two groups, with the importance of genera being evaluated based on the mean decrease accuracy (MDA). Results The oxygen consumption in the Legionella-positive group was significantly lower than that in the Legionella-negative group (mean values: 1.85 mg·L⁻¹ vs. 6.81 mg·L⁻¹, P< 0.05), while no significant differences were observed in other physicochemical indicators. The sequencing results revealed a total of 27 bacterial phyla and 454 bacterial genera, with Proteobacteria (63.00%) being the dominant phylum. The dominant genera included Pelomonas (8.50%), Acidovorax (8.13%), Mycobacterium (7.93%), and Acinetobacter (6.59%). The α-diversity analysis indicated that bacterial community richness (Chao1 and ACE indices) was significantly higher in the Legionella-positive group than in the Legionella-negative group (P<0.01). The β-diversity analysis showed no significant difference in the bacterial community structure between the two groups (P>0.05). The RDA analysis demonstrated that the bacterial community diversity was positively correlated with pH and negatively correlated with oxygen consumption and free residual chlorine. The RDA1 and RDA2 explained 23.92% and 21.30% of the bacterial community diversity, respectively. The random forest model identified 20 key genera significantly influencing the microbial community distribution between the two groups, including unclassified_Bradyrhizobiaceae (MDA=2.42), Meiothermus (MDA=2.37), and Flavihumibacter (MDA=2.26). Conclusion The diversity of bacterial communities in public bath water is influenced by pH, oxygen consumption, and free residual chlorine. Samples contaminated with Legionella pneumophila exhibit greater microbial richness and contain characteristic key bacterial genera that contribute to community differences. Machine learning random forest technology helps identify these distinctive key bacterial genera. The findings provide a basis for carrying out risk early warning strategies in such settings.

Objective: To investigate the association between brain and muscle Arnt like protein 1( BMAL 1) gene and ambulatory blood pressure (ABP) parameters in college students, and to explore the influence of the interaction between the gene and weight status on the ABP levels, so as to provide reference for early prevention of chronic cardiovascular diseases related to hypertension. Methods: During September 2022 to June 2024, a total of 510 college students were recruited from a university in Changsha, China for on site questionnaire investigation, physical examination, ABP monitoring and genotyping testing. Multiple linear regression method was used to explore the association between the BMAL 1 gene and ABP indicators. An interaction term was included in the general linear model of multiple linear regression to analyze the interactive effects of BMAL 1 gene polymorphisms and weight status on ABP levels. Results: The prevalence of abnormal 24 hour blood pressure, abnormal daytime blood pressure, and abnormal nighttime blood pressure were 3.3%, 3.1%, and 3.9%, respectively. Multiple linear regression analysis showed that the BMAL 1/rs7950226 polymorphism was positively correlated with the 24 hour diastolic blood pressure(DBP) and the daytime DBP level after adjusting for gender, age, and body mass index( β =0.87,0.99, both P <0.05). Also, interaction between BMAL 1/ rs 11022775 and weight status on the nighttime DBP level ( P interaction =0.03) was found. The CC genotype carriers had significantly higher nighttime DBP level ( β =3.52, P <0.05) among overweight/obesity college students, but no differences in nocturnal DBP levels were observed between CC genotype carriers and TT+CT genotype carriers among college students with normal body weight( P > 0.05). Conclusions The rs 7950226 polymorphism is associated with 24 hours DBP and daytime DBP levels. Significant interaction between rs 11022775 with weight status on the nighttime DBP level is found among college students.

Objective: To investigate the heritability of axial length (AL) by comparing the consistency of AL in monozygotic (MZ) and dizygotic (DZ) twins, thereby providing data support for research on the genetic mechanisms and prevention and control practices of myopia in children and adolescents. Methods: A total of 137 twin pairs aged 3 to 16 years who attended the Optometry Center of the First Affiliated Hospital of Anhui Medical University from January 2021 to December 2024 were enrolled, comprising 79 DZ pairs and 58 MZ pairs. Biometry was used to measure children s axial length, and structural equation modeling within the OpenMx package was applied to calculate the heritability of axial length. Results: The overall mean axial length of the twin cohort was (23.7±1.5) mm. The intraclass correlation coefficient (ICC) for axial length in all twins was 0.82 (95% CI =0.76-0.87) ( P < 0.05 ). The ICC for MZ twins was 0.97 (95% CI =0.94-0.98), which was higher than that for DZ twins ( ICC =0.71, 95% CI =0.58-0.80) (both P <0.05). Maximum likelihood estimation using OpenMx revealed that the additive genetic plus unique environmental (AE) model provided the best fit. Specifically, additive genetic factors (A) accounted for 93.63% (95% CI =90.31%-95.69%) of the total variance, while unique environmental factors (E) accounted for 6.37% (95% CI =4.31%-9.69%). Conclusion Axial length in children is predominantly influenced by genetic factors, while unique environmental factors also play a contributory role.

Objective To understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

To address the scarcity of annotated data for pediatric abdominal CT imaging and the insufficient generalization capability of existing models, we constructed a self-supervised pretraining architecture tailored for pediatric CT domain adaptation based on the visual foundation model DINOv3, and validated its performance in the task of pediatric abdominal multi-organ segmentation.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

ObjectiveTo investigate the mechanism by which Feixin decoction treats hypoxic pulmonary hypertension （HPH） by regulating the peroxisome proliferator-activated receptor gamma （PPARγ）/nuclear factor-kappa B （NF-κB）/NOD-like receptor pyrin domain containing 3 （NLRP3） signaling pathway. MethodsForty-eight male SD rats were randomly allocated into normal， hypoxia， and low-， medium- and high-dose （5.85， 11.7， 23.4 g·kg^-1， respectively） Feixin decoction groups， with 8 rats in each group. Except the normal group， the remaining five groups were placed in a hypoxia chamber with an oxygen concentration of （10.0±0.5）% for 8 h per day， 28 days， and administrated with corresponding drugs during the modeling process. After 4 weeks of treatment， echocardiographic parameters ［pulmonary artery acceleration time （PAT）， pulmonary artery ejection time （PET）， right ventricular anterior wall thickness （RVAWd）， and tricuspid annular plane systolic excursion （TAPSE）］ were measured for each group. The right ventricular systolic pressure （RVSP） was measured by the right heart catheterization method， and the right ventricular hypertrophy index （RVHI） was calculated by weighing the heart. The pathological changes in pulmonary arterioles were observed by hematoxylin-eosin staining. The co-localization of α-smooth muscle actin （α-SMA） with NLRP3， N-terminal gasdermin D （N-GSDMD）， and cysteinyl aspartate-specific proteinase-1 （Caspase-1） in pulmonary arteries was detected by immunofluorescence. The protein levels of PPARγ， NF-κB， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， N-GSDMD， interleukin-1β （IL-1β）， interleukin-18（IL-18）， and cleaved Caspase-1 in the lung tissue was determined by Western blot. The ultrastructural changes in pulmonary artery smooth muscle cells （PASMCs） were observed by transmission electron microscopy. ResultsCompared with the normal group， the hypoxia group showed increased RVSP and RVHI （P<0.01）， decreased right heart function （P<0.01）， increased pulmonary vascular remodeling （P<0.01）， increased co-localization of α-SMA with NLRP3， N-GSDMD， and Caspase-1 in pulmonary arterioles （P<0.01）， up-regulated protein levels of NF-κB， NLRP3， ASC， N-GSDMD， IL-1β， IL-18， and cleaved Caspase-1 in the lung tissue （P<0.05， P<0.01）， a down-regulated protein level of PPARγ （P<0.05， P<0.01）， and pyroptosis in PASMCs. Compared with the hypoxia group， Feixin decoction reduced RVSP and RVHI， improved the right heart function and ameliorated pulmonary vascular remodeling （P<0.05， P<0.01）， decreased the co-localization of α-SMA with NLRP3， N-GSDMD， and Caspase-1 （P<0.05， P<0.01）， down-regulated the protein levels of NF-κB， NLRP3， ASC， N-GSDMD， IL-1β， IL-18， and cleaved Caspase-1 in the lung tissue （P<0.05， P<0.01）， up-regulated the protein level of PPARγ （P<0.05， P<0.01）， and alleviated pyroptosis in PASMCs. ConclusionFeixin decoction can ameliorate pulmonary vascular remodeling and right heart dysfunction in chronically induced HPH rats by regulating pyroptosis in PASMCs through the PPARγ/NF-κB/NLRP3 pathway.