ObjectiveTo explore the association between metabolic score for visceral fat（METS-VF） and cardiometabolic multimorbidity（CMM） as well as cognitive function in the elderly population， revealing the role of METS-VF as an assessment tool in this population， and providing evidence and new perspectives for the early screening and intervention strategies of CMM and cognitive function. MethodsThis study is based on cross-sectional data from the Hubei memory and aging cohort study（HMACS）， which includes elderly individuals aged 60 and above. The METS-VF index was calculated and divided into quartiles（Q1-Q4）. CMM was assessed through a questionnaire， defining CMM as the coexistence of at least two of the following conditions： diabetes， heart disease， and stroke. Standardized cognitive tests were used to evaluate overall cognition， memory， language， executive function， and attention. Logistic and Linear regression analysis was performed to explore the association between METS-VF and the risk of CMM as well as cognitive function. A restricted cubic spline was used to test the dose-response relationship， and subgroup analysis was conducted to assess the variation of METS-VF in different populations. Additionally， multinomial unordered logistic regression was used to evaluate the performance of METS-VF in predicting CMM morbidity patterns. ResultsA total of 3 790 elderly participants aged 60 and above were included. After adjusting for confounding factors， compared with the lowest quartile（Q1）， the Q4 group of METS-VF was associated with a higher risk of CMM ［OR=3.00，95%CI （2.18， 4.16）］， and with lower scores in overall cognition ［b=-0.12， 95%CI （-0.21，-0.04）］， attention ［b=-0.14， 95%CI （-0.24，-0.05）］， and executive function ［b=-0.10， 95%CI （-0.20，-0.00）］. Dose-response analysis did not show a nonlinear relationship between METS-VF and either CMM or cognitive function （P for nonlinear > 0.05）. Subgroup analysis revealed that the male group with higher METS-VF had lower overall cognitive scores， while the female group showed lower executive function scores （P for interaction ＜ 0.05 for both）. Multinomial unordered logistic regression analysis indicated that the METS-VF index was associated with an increased risk of diabetes with coronary heart disease ［OR=2.62，95%CI （1.66， 4.15）］ or concomitant stroke ［OR=2.66，95%CI （1.73， 4.09）］， especially in the group with both diabetes and stroke and coronary heart disease， where the association between METS-VF and the risk was strongest. ConclusionsAn increased METS-VF is associated with a higher risk of CMM and lower scores in overall cognition， attention， and executive function in the elderly population. Diabetes may be a key driving factor in the multimorbidity model. The METS-VF index can serve as a dual-purpose tool for screening cardiovascular metabolic risks and assessing cognitive function.

In November 23, 2023, a patient with 9 digits traumatic crush injury by machine compression was emergently admitted to the Department of Hand and Microsurgery, Sichuan Modern Hospital. Emergency procedures included amputation the distal stumps and replantation of proximal phalanges of left ring and little fingers. Wounds in both hands were temporarily covered with bone cement. On December 4, 2023, reconstruction of 5 digits were performed. Digital defects were: Type Ⅲ defects of left index and middle fingers and right thumb and index fingers and Type IV defect of right middle finger. All 5 reconstructed digits survived. Subsequent refinements yielded favourable outcomes and all donor toes were preserved completely. At the 14-month follow-up, the reconstructed digits exhibited satisfactory appearance and length without difficulties in daily life and at work.

Precise management of the activity dose is a core component of the(early mobilization,EM)plan for ICU patients.However,the lack of clinical practice guidelines related to EM dose of existing programs hinders the implementation and development of EM in ICU patients to some extent.Therefore,this review focuses on 4 aspects,covering the definition of activity dose,assessment tools,the current clinical implementation status,and implications for future nursing.The aim is to systematically review the assessment tools and intervention strategies for the activity dose of EM in ICU patients,providing a reference for optimization of EM programs.

Objective To analyze the influencing factors of cholecystolithiasis among Tibetan residents living at extreme altitude.Methods From Aug.10 to 17,2023,Nima County People's Hospital in Nagqu,in collaboration with Tibetan-aid doctors,organized physical examinations for Tibetan residents in Jiwa Township.The physical examination data were collected,and the general information and dietary habits of the examinees were recorded.Based on the results of abdominal ultrasound examination,the 448 enrolled participants were assigned to a cholecystolithiasis group(106 cases)and a non-cholecystolithiasis group(342 cases).Additionally,multivariable logistic regression analysis was conducted to investigate the factors influencing the occurrence of cholecystolithiasis among Tibetan residents.Results There were significant differences in age,weight,body mass index,systolic blood pressure,diastolic blood pressure,finger pulse oxygen,and educational level between the 2 groups(all P＜0.05).The results of multivariate logistic regression analysis showed that age(odds ratio[OR]=1.062,95％confidence interval[CI]1.020-1.106),weight(OR=1.120,95％CI 1.067-1.176),smoking(OR=4.751,95％CI 1.627-13.869),cholesterol(OR=1.429,95％CI 1.129-1.810),amount of butter tea drink(OR=2.123,95％CI 1.656-2.721),alanine transaminase(OR=1.045,95％CI 1.028-1.062),low educational level(OR=6.994,95％CI 2.615-18.702),fatty liver(OR=4.409,95％CI 1.499-12.968)and meat-based diet(OR=3.725,95％CI 1.255-11.055)were correlated with cholecystolithiasis.Conclusion The incidence of cholecystolithiasis among Tibetan residents living at extreme altitude may be related to various factors mentioned above,which provides clues for the prevention,early diagnosis,and treatment of local cholecystolithiasis.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective:To investigate the application of time series models in forecasting pre-hospital emergency ambulance trips in Handan City and develop the LPro ensemble model for improved prediction accuracy to support emergency resource allocation.Methods:Pre-hospital emergency data from Handan Emergency Medical Command Center (2019-2023) were retrospectively analyzed. From 324 799 original records, 289 949 valid records were included after cleaning. The training set (2019-2022: 215 918 records) included 35 527 records in 2019, 52 015 in 2020, 61 836 in 2021, and 66 540 in 2022. The validation set (2023) contained 74 031 records. ARIMA, linear trend seasonal, exponential smoothing, and Prophet models were fitted to the training set. The LPro ensemble model was constructed using MAPE-based weighting (linear trend seasonal model: 0.38, Prophet: 0.62). Performance metrics included MAPE, RMSE, MAE, and R 2. Results:Data showed annual growth (compound annual growth rate 23.27%) and seasonal patterns (October peaks, February troughs). Ambulance dispatches increased annually with monthly cyclical patterns. For 2023 validation predictions: ARIMA (MAPE 8.76%, RMSE 619, MAE 491, R 2 0.4563), linear trend seasonal (MAPE 9.83%, RMSE 671, MAE 545, R 2 0.3608), Prophet (MAPE 8.43%, RMSE 562, MAE 503, R 2 0.5513), exponential smoothing (MAPE 8.08%, RMSE 643, MAE 410, R 2 0.4124). LPro model showed superior performance (MAPE 7.05%, RMSE 491, MAE 393, R 2 0.6570), with 16.37% lower MAPE, 12.63% lower RMSE, 21.87% lower MAE, and 19.17% higher R 2 versus Prophet. Conclusion:The LPro ensemble model substantially enhances prediction accuracy and reliability, offering scientific support for emergency resource optimization and dispatch scheduling in Handan City.