ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.

Objective:To discuss the influencing factors of angina pectoris in the patients with diabetes mellitus(DM),to construct a Bayesian network model to explore the network relationships among the influencing factors,and to predict the risk of angina pectoris in the patients with DM.Methods:Based on the UK Biobank(UKB)database,the Logistic regression aralysis model was used to screen the influencing factors of angina pectoris in the patients with DM.The taboo search algorithm was used for structure learning,and the Bayesian parameter estimation method was used for parameter learning to construct the Bayesian network model.Results:A total of 22 712 DM patients were included.The influencing factors of angina pectoris in the patients with DM included 14 variables:gender,age,body mass index(BMI),triglycerides(TG),total cholesterol(TC),glycated hemoglobin(HbA1c),hypertension,maternal smoking around delivery,smoking status,alcohol consumption,regular exercise,insomnia,sleep duration,and childhood relative body size(P<0.05).A Bayesian network model was constructed with 15 nodes and 22 directed edges.Among them,age,HbA1c,hypertension,regular exercise,BMI,and sleep duration were directly associated with the occurrence of angina pectoris in the patients with DM,while gender,smoking status,alcohol consumption,TC,TG,insomnia,childhood relative body size,and maternal smoking around delivery were indirectly associated with the occurrence of angina pectoris in the patients with DM.Conclusion:Age,HbA1c,hypertension,regular exercise,BMI,and sleep duration are direct influencing factors of angina pectoris in the patients with DM.Controlling HbA1c,blood pressure,and BMI levels,engaging in regular exercise,and maintaining appropriate sleep duration are beneficial for reducing the risk of angina pectoris in the patients with DM.

Objective To evaluate the predictive value of neutrophil/high-density lipoprotein cholesterol ratio(NHR)combined with monocyte/lymphocyte ratio(MLR)for early rebleeding after endoscopic treatment in patients with cirrhosis complicated by acute esophagogastric variceal bleeding(AEVB).Methods A total of 228 patients with cirrhosis complicated by AEVB were included in this study.According to the occurrence of early rebleeding,patients were divided into the rebleeding group(96 cases)and the non-rebleeding group(132 cases).General information and laboratory indicators of both groups were collected,and the End-Stage Liver Disease(MELD)score,Child-Turcotte-Pugh(CTP)score,Fibrosis-4(FIB-4)index,NHR,and MLR were calculated.Logistic regression analysis was used to identify the risk factors for early rebleeding in patients with cirrhosis complicated by AEVB.A nomogram model based on NHR and MLR was constructed to predict the risk of early rebleeding.The predictive performance and goodness of fit of the model were evaluated using receiver operating characteristic(ROC)curve,Hosmer-Lemeshow test,Net Reclassification Index(NRI)and Integrated Discrimination Improvement(IDI).Results Compared with the non-rebleeding group,systolic blood pressure,platelet count(PLT),albumin/globulin ratio(A/G)and low-density lipoprotein cholesterol(LDL-C)were decreased in the rebleeding group,while total bile acids(TBA),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total bilirubin(TBIL),activated partial thromboplastin time(APTT),thrombin time(TT),international normalized ratio(INR),Fibrosis-4(FIB-4),NHR,MLR,MELD score and CTP score were increased(P＜0.05).NHR was positively correlated with AST,TBIL and INR(P＜0.05).MLR was negatively correlated with PLT,and positively correlated with AST,TBIL and FIB-4(P＜0.05).Logistic regression analysis results showed that prolonged TT,elevated NHR and MLR were independent risk factors for early rebleeding in patients with cirrhosis complicated by AEVB.The nomogram model based on NHR and MLR to predict early rebleeding had an area under the curve of 0.810(95%CI:0.754-0.866).The Hosmer-Lemeshow test suggested that the model fit well.IDI and NRI analyse showed that the combination of NHR and MLR had better predictive value for the early rebleeding than that of MELD score and CTP score.Conclusion NHR and MLR are effective indicators for predicting early rebleeding after endoscopic treatment in patients with cirrhosis complicated by AEVB.They are helpful in the early identification of high-risk patients and provide a reference for clinical intervention.

Background: Rheumatoid arthritis (RA) is a systemic inflammatory disease primarily affecting the joints of the limbs. As the disease progresses, it can involve multiple organ systems. Interstitial lung disease (ILD) is the most common pulmonary manifestation of RA. Reported animal models of RA-ILD include adjuvant-induced arthritis (AA), collagen-induced arthritis (CIA), and transgenic mouse arthritis. However, the establishment criteria and evaluation methods for these models lack uniform standards, and they fail to fully replicate the clinicopathological characteristics of RA-ILD. This limitation significantly hinders research into the pathogenesis and development of therapeutic drugs for RA-ILD. Objective: The aim of the study was to review literature in China and abroad on RA-ILD animal models, analyze current research progress, identify existing issues, and propose research recommendations. Methods: Literature searches were conducted using the English keywords “rheumatoid arthritis, interstitial lung disease, model” and the Chinese keywords “(rheumatoid arthritis), (interstitial lung disease), (model)” or “(rheumatoid arthritis), (lung interstitial lesions), (model).” The search was performed in PubMed, Web of Science, CNKI, Wanfang Data, and VIP (China Science and Technology Journal Database) for articles published before November 2024. A total of 41 articles were included. Results and conclusions: The CIA model and the CIA model combined with bleomycin are commonly used due to their similarities to the histopathology and disease manifestations of human RA-ILD. Additionally, these models have appropriate cost and modeling duration, along with a high success rate, making them preferable choices. Transgenic animal models exhibit pathological features similar to the nonspecific interstitial pneumonia subtype of human RA-ILD and are useful for studying the genetic effects on RA-ILD. However, they have drawbacks such as high economic costs, long modeling durations, and a low success rate in some cases. The AA model is easy to establish, requires a short modeling period, and has low experimental costs. However, it lacks the chronic pathological development characteristic of human RA and exhibits a degree of self-limitation in lesion progression. Among other models, the comprehensive HLA-DQ8 transgenic mouse model can be used to study the combined effects of genetic and environmental factors on RA-ILD. The collagen autoantibody-induced arthritis model combined with bleomycin has a short modeling period, but it does not align well with the disease course of RA-ILD. These established animal models provide valuable insights into the pathogenesis of RA-ILD, the identification of novel biomarkers, and the development of new therapeutic approaches. Future research should focus on identifying an animal model that better replicates the physiological and pathological changes of clinical RA-ILD while being more convenient, cost-effective, and comprehensive in reflecting disease progression.

Objective To investigate the risk of chronic obstructive pulmonary disease(COPD)after asthma and explore factors influen-cing the onset and progression of asthma in patients with COPD.Methods A follow-up cohort was established based on the United Kingdom Biobank(UKB)database.The risk of asthma and COPD was predicted,and the influencing factors were analyzed using a mul-tistate model(MSM).Results Without considering the influence of covariates,the cumulative risk from COPD to mortality was the highest,followed by asthma to COPD,and asthma to mortality.Advanced age,male,diabetes mellitus(DM),high waist-to-hip ratio,hyper-tension,increased Townsend deprivation index,increased frequency of smoking,and family history were risk factors for developing COPD in the asthmatic population.Advanced age,male,DM,high waist-to-hip ratio,hypertension,increased Townsend deprivation index,and in creased frequency of smoking were risk factors for mortality in the asthmatic population.Advanced age,male,and DM and increased Townsend deprivation index were risk factors for mortality in the COPD population.Conclusion Advanced age,male,DM,high waist-to-hip ratio,hypertension,increased Townsend deprivation index,increased smoking frequency,and family history increased the risk of COPD in the asthmatic population.This MSM can be used to predict the influencing factors and degree of COPD after asthma,and reveal the change law of disease progression.

Objective:To analyze the key β-amyloid protein (Aβ) deposition in brain regions affecting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).Methods:Based on the positron emission tomography data of Aβ in the Alzheimer's disease neuroimaging initiative database, the penalized generalized estimating equation (PGEE) and the mixed effects regression forest algorithm (MERF) were used to conduct dimensionality reduction analysis on 164 brain regions with Aβ deposition. Additionally, a multivariate longitudinal data joint model was used to screen the key Aβ deposition brain regions that influence the progression from MCI to AD.Results:Five key brain regions were commonly screened out by the PGEE and MERF models, they were the right prefrontal orbital cortex, the left superior temporal sulcus shore cortex, the right medial orbitofrontal cortex, the left putamen, and the right transverse temporal cortex, respectively. The results of the multivariate longitudinal data joint model based on these 5 Aβ deposition brain regions showed that, except the left superior temporal sulcus shore cortex, the longitudinal change trajectories of the other 4 Aβ deposition brain regions all affected the progression from MCI to AD ( P<0.05). Conclusion:The Aβ deposition in the right prefrontal orbital cortex, right medial orbitofrontal cortex, left putamen and right transverse temporal cortex affect the progression from MCI to AD.

Objective:To examine the relationships between perceived organizational support in job resources, self-efficacy in personal resources, and teaching commitment based on the job requirement-resource model, and to explore the path of influencing clinical teachers' teaching commitment.Methods:Using the convenience sampling method, 463 clinical teachers from 16 university-affilicated hospitals in Shandong Province, China were selected as the study subjects. Surveys were conducted using the Perceived Organizational Support Scale, Self-efficacy Scale, and Teaching Commitment Scale. A statistical analysis was performed using SPSS 26.0. A structural equation model was constructed using AMOS 26.0 to test the mediation effect.Results:①The level of teaching commitment of clinical teachers was relatively high, with a total score of (45.25±9.21) points. The total score of perceived organizational support was (38.22±9.75) points, and the total score of self-efficacy was (112.27±17.30) points. ②Perceived organizational support and self-efficacy were positively correlated with teaching commitment ( r=0.59, r=0.65, both P<0.01). ③ Perceived organizational support had significant and direct positive effects on teaching commitment and self-efficacy ( β=0.36, β=0.51, both P<0.01). Self-efficacy significantly and positively affected teaching commitment ( β=0.47, P<0.01). Self-efficacy played a significant and partial mediating role in the effect of perceived organizational support on teaching commitment (standardized mediating effect value of 0.239, P<0.01), and the mediating effect percentage was 39.83%. Conclusions:Perceived organizational support not only directly predicts clinical teachers' teaching commitment, but also indirectly affects teaching commitment through self-efficacy. University-affiliated hospital administrators should pay attention to clinical teachers' perceived organizational support to improve their self-efficacy and teaching commitment, thereby ensuring teaching quality.

Objective:To systematically evaluate the factors influencing the physical activity levels of patients with chronic obstructive pulmonary disease (COPD) .Methods:Computer retrieval of literature related to the influencing factors of physical activity in patients with COPD in PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang, VIP Database, and China Biology Medicine disc, and the search period was from the establishment of the databases to March 2024. Two researchers independently screened the literature and conducted quality assessment according to inclusion and exclusion criteria. Meta-analysis was conducted using RevMan 5.4 software.Results:A total of 14 studies were included. The results of the Meta-analysis showed that gender [ OR=1.54, 95% CI (1.21, 1.96) ], degree of dyspnea [ OR=1.74, 95% CI (1.37, 2.21) ], fatigue [ OR=2.06, 95% CI (1.48, 2.87) ], and depression [ OR=1.63, 95% CI (1.30, 2.04) ] were influencing factors of physical activity levels in COPD patients ( P<0.05) . Conclusions:The physical activity levels of COPD patients are influenced by gender, degree of dyspnea, fatigue, and depression. Healthcare providers should develop personalized intervention goals and plans to improve patients' participation in and adherence to physical activity.

Objective:To explore the latent classes of intrinsic capacity and their influencing factors among hospitalized older adults, providing a basis for developing targeted intervention strategies.Methods:A convenience sampling method was used to recruit 371 hospitalized older adults from Beijing Hospital between April 2022 and July 2023. The integrated care for older people comprehensive assessment tool was used to assess the intrinsic capacity of hospitalizd older adults, along with grip strength tests. Latent class analysis (LCA) was employed to classify the intrinsic capacity of hospitalizd older adults, and multinomial Logistic regression analysis was conducted to explore the influencing factors of different latent classes.Results:Among the 371 older adults, 78.98% (293/371) exhibited declines in various dimensions of intrinsic capacity, with impairment detection rates of 36.39% (135/371) in cognition, 34.23% (127/371) in mobility, 21.02% (78/371) in vitality, 33.69% (125/371) in psychological well-being, and 47.98% (178/371) in sensory function. The LCA results identified three latent classes of intrinsic capacity: high mobility-moderate sensory group (39.08%, 145/371) , low cognition-high mobility group (26.68%, 99/371) , and low mobility group (34.23%, 127/371) . Multinomial Logistic regression analysis revealed that age, gender, monthly family income, polypharmacy, and grip strength were influencing factors of the latent classes of intrinsic capacity ( P<0.05) . Conclusions:The intrinsic capacity of hospitalized older adults is heterogeneous. It is essential to promptly identify the characteristics of intrinsic capacity in different groups, with particular attention to older adults in the low cognition-high mobility group and low mobility group, and implement targeted interventions to prevent and improve adverse outcomes.

Objective:To analyze the key β-amyloid protein (Aβ) deposition in brain regions affecting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD).Methods:Based on the positron emission tomography data of Aβ in the Alzheimer's disease neuroimaging initiative database, the penalized generalized estimating equation (PGEE) and the mixed effects regression forest algorithm (MERF) were used to conduct dimensionality reduction analysis on 164 brain regions with Aβ deposition. Additionally, a multivariate longitudinal data joint model was used to screen the key Aβ deposition brain regions that influence the progression from MCI to AD.Results:Five key brain regions were commonly screened out by the PGEE and MERF models, they were the right prefrontal orbital cortex, the left superior temporal sulcus shore cortex, the right medial orbitofrontal cortex, the left putamen, and the right transverse temporal cortex, respectively. The results of the multivariate longitudinal data joint model based on these 5 Aβ deposition brain regions showed that, except the left superior temporal sulcus shore cortex, the longitudinal change trajectories of the other 4 Aβ deposition brain regions all affected the progression from MCI to AD ( P<0.05). Conclusion:The Aβ deposition in the right prefrontal orbital cortex, right medial orbitofrontal cortex, left putamen and right transverse temporal cortex affect the progression from MCI to AD.