Objective:To develop and validate a 30-day readmission risk prediction model for patients with chronic obstructive pulmonary disease (COPD) using multiple machine learning algorithms.Methods:Convenience sampling was used to select 1 450 COPD patients hospitalized at Tianjin Medical University General Hospital from January 2017 to December 2023 as study subjects. Twenty-nine variables associated with readmission were included. LASSO was used to screen for primary characteristic variables associated with 30-day readmission. The 1 450 patients were divided into a training set ( n=870) and a test set ( n=580) in a 6∶4 ratio. Ten machine learning methods, including random forest, AdaBoost, extreme radient Boosting (XGB), decision tree and so on, were used for model training and testing to identify the optimal prediction model. The optimal model and SHAP were employed to analyze key features and rank characteristic importance. Results:Among 1 450 COPD patients, the 30-day readmission rate was 24.48% (355/1 450). There were no significant differences in the general information between patients in test set and training set ( P>0.05). LASSO regression analysis identified seven variables with the highest predictive value, namely regular weekly exercise, hospital stay, mean arterial pressure, forced expiratory volume in one second/forced vital capacity (FEV1/FVC), C-reactive protein, body mass index, and medication adherence. Machine learning showed that in the training set, XGB had the highest area under the receiver operating characteristic curve ( AUC), sensitivity, and F1 score of 0.943, 0.926, and 0.930, respectively. In the test set, the AUC and accuracy of XGB were 0.882 and 0.858, respectively, and XGB's various scores showed that it had good generalization and predictive performance. XGB analysis showed that medication adherence, FEV1/FVC, and regular weekly exercise were negatively correlated with the 30-day readmission risk, while body mass index, C-reactive protein, mean arterial pressure, and hospital stay were positively correlated. The characteristics ranked in order of importance were medication adherence, body mass index, C-reactive protein, mean arterial pressure, FEV1/FVC, hospital stay and regular weekly exercise. Conclusions:The XGB model has strong predictive performance and good generalization ability, which can effectively predict the 30-day readmission risk of COPD patients, assist in clinical identification of high-risk patients, implement nursing interventions, and reduce readmission rates.

Objective To identify facilitators and barriers to colorectal cancer screening in community healthcare and propose effective implementation strategies.Methods From February to July 2024,a survey based on the consolidated framework for implementation research(CFIR)was conducted in 8 community health centers in Harbin,using purposive and convenience sampling to select 108 nurses and 550 residents.Binary logistic regression was used to analyze factors affecting nurses'screening knowledge and residents'behaviors.Semi-structured interviews with 21 nurses and 23 residents were analyzed using inductive-deductive methods.Results In the innovation domain,clear goals and free screening facilitated the implementation,while complex processes and resource shortages were barriers.Externally,policy support,funding,and collaboration promoted the screening,but funding gaps and poor communication hindered it.Internally,organizational culture and leadership were positive,while nurse training and teamwork were insufficient.Mixed-method results showed that motivation,ability,opportunity,and needs of nurses and residents significantly impacted screening.Regular feedback and teamwork aided the implementation,but complexity and communication gaps remained obstacles.Conclusion This study analyzed the facilitators and barriers to the screening implementation from the perspectives of community nurses and participants.Recommendations include simplifying processes,optimizing tools and policies,strengthening nurse training and health education,improving communication feedback,and improve outcomes,thereby increasing the participation rate of residents in colorectal cancer screening.

Objective To identify facilitators and barriers to colorectal cancer screening in community healthcare and propose effective implementation strategies.Methods From February to July 2024,a survey based on the consolidated framework for implementation research(CFIR)was conducted in 8 community health centers in Harbin,using purposive and convenience sampling to select 108 nurses and 550 residents.Binary logistic regression was used to analyze factors affecting nurses'screening knowledge and residents'behaviors.Semi-structured interviews with 21 nurses and 23 residents were analyzed using inductive-deductive methods.Results In the innovation domain,clear goals and free screening facilitated the implementation,while complex processes and resource shortages were barriers.Externally,policy support,funding,and collaboration promoted the screening,but funding gaps and poor communication hindered it.Internally,organizational culture and leadership were positive,while nurse training and teamwork were insufficient.Mixed-method results showed that motivation,ability,opportunity,and needs of nurses and residents significantly impacted screening.Regular feedback and teamwork aided the implementation,but complexity and communication gaps remained obstacles.Conclusion This study analyzed the facilitators and barriers to the screening implementation from the perspectives of community nurses and participants.Recommendations include simplifying processes,optimizing tools and policies,strengthening nurse training and health education,improving communication feedback,and improve outcomes,thereby increasing the participation rate of residents in colorectal cancer screening.

Objective:To investigate the metabolic pathways and potential molecular targets associated with dapagliflozin in the treatment of type 2 diabetes mellitus.Methods:Plasma samples from patients with type 2 diabetes mellitus were collected before and after 12 months of dapagliflozin treatment and analyzed using UPLC-VION IMS Q-Tof-based metabolomics and timsTOF Pro2 diaPASEF-based proteomics. Multivariate statistical analyses were performed to identify significant differences pre- and post-treatment. Correlation analysis was then conducted to assess relationships between differentially expressed metabolites and proteins closely associated with type 2 diabetes mellitus. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analyses were used to construct metabolic pathway maps and predict therapeutic targets.Results:After 12 months of dapagliflozin treatment, 162 differential metabolites were identified, with 59 upregulated and 103 downregulated. A total of 440 differentially expressed proteins were detected, of which 272 were upregulated and 168 were downregulated. The main classes of differential metabolites included sphingolipids, glycerophospholipids, and glycosphingolipids. Key differentially expressed proteins included importin subunit alpha-11, synemin, Janus kinase 1, and far upstream element-binding protein 2. Correlation analysis revealed 98 shared enriched pathways between differential metabolites and proteins, involving neurotrophin signaling, chemokine signaling, and B cell receptor signaling pathways. Metabolic pathway analysis suggested that dapagliflozin might regulate insulin secretion by modulating glucose-dependent insulinotropic polypeptide, calmodulin-dependent protein kinase, and diacylglycerol levels.Conclusion:Dapagliflozin may exert therapeutic effects in type 2 diabetes mellitus through multiple mechanisms, including the modulation of metabolic and proteomic profiles, participation in key cellular signaling pathways, and regulation of insulin secretion.

Objective:To evaluate the function of right atrium(RA)in patients with systemic lupus erythematosus(SLE)by using two-dimensional speckle tracking echocardiography(2D-STE),and to analyze the correlation between the indicators that might affect the RA function.Methods:A total of 81 patients,who were diagnosed as SLE in the First Hospital of Shanxi Medical University from September 2022 to February 2023,were selected as the case group.According to the pulmonary artery systolic pressure(PASP),the case group was further divided into group A(PASP≤30 mmHg),group B(30 mmHg

Objective:To develop and validate a 30-day readmission risk prediction model for patients with chronic obstructive pulmonary disease (COPD) using multiple machine learning algorithms.Methods:Convenience sampling was used to select 1 450 COPD patients hospitalized at Tianjin Medical University General Hospital from January 2017 to December 2023 as study subjects. Twenty-nine variables associated with readmission were included. LASSO was used to screen for primary characteristic variables associated with 30-day readmission. The 1 450 patients were divided into a training set ( n=870) and a test set ( n=580) in a 6∶4 ratio. Ten machine learning methods, including random forest, AdaBoost, extreme radient Boosting (XGB), decision tree and so on, were used for model training and testing to identify the optimal prediction model. The optimal model and SHAP were employed to analyze key features and rank characteristic importance. Results:Among 1 450 COPD patients, the 30-day readmission rate was 24.48% (355/1 450). There were no significant differences in the general information between patients in test set and training set ( P>0.05). LASSO regression analysis identified seven variables with the highest predictive value, namely regular weekly exercise, hospital stay, mean arterial pressure, forced expiratory volume in one second/forced vital capacity (FEV1/FVC), C-reactive protein, body mass index, and medication adherence. Machine learning showed that in the training set, XGB had the highest area under the receiver operating characteristic curve ( AUC), sensitivity, and F1 score of 0.943, 0.926, and 0.930, respectively. In the test set, the AUC and accuracy of XGB were 0.882 and 0.858, respectively, and XGB's various scores showed that it had good generalization and predictive performance. XGB analysis showed that medication adherence, FEV1/FVC, and regular weekly exercise were negatively correlated with the 30-day readmission risk, while body mass index, C-reactive protein, mean arterial pressure, and hospital stay were positively correlated. The characteristics ranked in order of importance were medication adherence, body mass index, C-reactive protein, mean arterial pressure, FEV1/FVC, hospital stay and regular weekly exercise. Conclusions:The XGB model has strong predictive performance and good generalization ability, which can effectively predict the 30-day readmission risk of COPD patients, assist in clinical identification of high-risk patients, implement nursing interventions, and reduce readmission rates.

Objective:To evaluate the function of right atrium(RA)in patients with systemic lupus erythematosus(SLE)by using two-dimensional speckle tracking echocardiography(2D-STE),and to analyze the correlation between the indicators that might affect the RA function.Methods:A total of 81 patients,who were diagnosed as SLE in the First Hospital of Shanxi Medical University from September 2022 to February 2023,were selected as the case group.According to the pulmonary artery systolic pressure(PASP),the case group was further divided into group A(PASP≤30 mmHg),group B(30 mmHg

Objective:To investigate the metabolic pathways and potential molecular targets associated with dapagliflozin in the treatment of type 2 diabetes mellitus.Methods:Plasma samples from patients with type 2 diabetes mellitus were collected before and after 12 months of dapagliflozin treatment and analyzed using UPLC-VION IMS Q-Tof-based metabolomics and timsTOF Pro2 diaPASEF-based proteomics. Multivariate statistical analyses were performed to identify significant differences pre- and post-treatment. Correlation analysis was then conducted to assess relationships between differentially expressed metabolites and proteins closely associated with type 2 diabetes mellitus. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analyses were used to construct metabolic pathway maps and predict therapeutic targets.Results:After 12 months of dapagliflozin treatment, 162 differential metabolites were identified, with 59 upregulated and 103 downregulated. A total of 440 differentially expressed proteins were detected, of which 272 were upregulated and 168 were downregulated. The main classes of differential metabolites included sphingolipids, glycerophospholipids, and glycosphingolipids. Key differentially expressed proteins included importin subunit alpha-11, synemin, Janus kinase 1, and far upstream element-binding protein 2. Correlation analysis revealed 98 shared enriched pathways between differential metabolites and proteins, involving neurotrophin signaling, chemokine signaling, and B cell receptor signaling pathways. Metabolic pathway analysis suggested that dapagliflozin might regulate insulin secretion by modulating glucose-dependent insulinotropic polypeptide, calmodulin-dependent protein kinase, and diacylglycerol levels.Conclusion:Dapagliflozin may exert therapeutic effects in type 2 diabetes mellitus through multiple mechanisms, including the modulation of metabolic and proteomic profiles, participation in key cellular signaling pathways, and regulation of insulin secretion.

Digestive system tumors are malignant tumors with high malignancy,easy metastasis,and poor prognosis,which are difficult to diagnose due to the insidious onset.Most of the patients have reached the middle and advanced stages at the time of diagnosis,so the effect of treatment is usually not good.Studies have shown that the long non-coding RNA(lncRNA)small nucleolar RNA host gene 6(SNHG6)is highly expressed in a variety of solid tumors,and can participate in malignant biological processes such as tumor proliferation,invasion and migration.SNHG6 is closely related to the emergence of chemotherapy drug resistance.This article reviews the biological function and clinical significance of SNHG6 in 6 different tumors of the digestive system,and discusses its mechanism of action in tumorigenesis and development,in the hope to search for effective early diagnosis and treatment targets and thereby improve the survival rate of cancer patients.

Digestive system tumors are malignant tumors with high malignancy,easy metastasis,and poor prognosis,which are difficult to diagnose due to the insidious onset.Most of the patients have reached the middle and advanced stages at the time of diagnosis,so the effect of treatment is usually not good.Studies have shown that the long non-coding RNA(lncRNA)small nucleolar RNA host gene 6(SNHG6)is highly expressed in a variety of solid tumors,and can participate in malignant biological processes such as tumor proliferation,invasion and migration.SNHG6 is closely related to the emergence of chemotherapy drug resistance.This article reviews the biological function and clinical significance of SNHG6 in 6 different tumors of the digestive system,and discusses its mechanism of action in tumorigenesis and development,in the hope to search for effective early diagnosis and treatment targets and thereby improve the survival rate of cancer patients.