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: 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 explore the differences of total bile acid in carotid atherosclerosis(CAS)patients with different traditional Chinese medicine(TCM)constitution.Methods CAS patients who were treated in Tai'an Municipal Hospital from December 2022 to May 2023 were selected,clinical data of patients were collected and TCM constitution was identified,and differences in serum total bile acid among patients with different TCM constitutions were analyzed.Results A total of 212 CAS patients were included,including 151 patients with biased constitution.The top 3 biased constitution included yang-deficiency constitution(49 cases,23.1％),qi-depression constitution(25 cases,11.8％),blood-stasis constitution(19 cases,9.0％).The body mass index of patients with yin-deficiency constitution was significantly lower than that of other patients(P＜0.05).The total bile acid level of patients with yang-deficiency constitution was significantly higher than that of patients with normal constitution,qi-depression constitution,blood-stasis constitution,phlegm-dampness constitution,humid heat constitution(P＜0.05).Conclusion There are differences of total bile acid in CAS patients with different TCM constitution,and yang-deficiency constitution is closely related to total bile acid metabolism.

Ziziphi spinosae semen mainly contains contents of saponins,flavonoids,alkaloids and aliphatic acids.Meanwhile,it has a variety of activities such as sedative-hypnotic,anti-anxiety,anti-depression,nerve protection,cardiovascular and cerebrovascular protection,liver protection,and antioxidant,which is widely used in medicine,food,health food and other fields.The chemical constituents,pharmacological action and clinical application of Ziziphi spinosae semen were systematically summarized in this paper by reviewing the literature,in order to provide theoretical guidance for the sustainable development of the resources and the rational use of Ziziphi spinosae semen.

Hip fracture in the elderly is characterized by high incidence, high disability rate, and high mortality and has been recognized as a public health issue threatening their health. Surgery is the preferred choice for the treatment of elderly patients with hip fracture. However, lower extremity deep venous thrombosis (DVT) has an extremely high incidence rate during the perioperative period, and may significantly increase the risk of patients′ death once it progresses to pulmonary embolism. In response to this issue, the clinical guidelines and expert consensuses all emphasize active application of comprehensive preventive measures, including basic prevention, physical prevention, and pharmacological prevention. In this prevention system, basic prevention is the basis of physical and pharmacological prevention. However,there is a lack of unified and definite recommendations for basic preventive measures in clinical practice. To this end, the Orthopedic Nursing Professional Committee of the Chinese Nursing Association and Nursing Department of the Orthopedic Branch of the China International Exchange and Promotive Association for Medical and Health Care organized relevant nursing experts to formulate Expert consensus on perioperative basic prevention for lower extremity deep venous thrombosis in elderly patients with hip fracture ( version 2024) . A total of 10 recommendations were proposed, aiming to standardize the basic preventive measures for lower extremity DVT in elderly patients with hip fractures during the perioperative period and promote their subsequent rehabilitation.

Objective To establish an early prediction model for the diagnosis of severe acute pancreatitis based on the improved machine learning models,and to analyze its clinical value.Methods A case-control study was conducted on 352 patients with acute pancreatitis admitted to the Gastroenterology and Hepatobiliary Surgery Departments of the Army Medical Center of PLA and Emergency and Critical Care Medicine Department of No.945 Hospital of Joint Logistics Support Force of PLA from January 2014 to August 2023.According to the severity of the disease,the patients were divided into the severe group(n=88)and the non-severe group(n=264).The RUSBoost model and improved Archimead optimization algorithm was used to analyze 39 routine laboratory biochemical indicators within 48 h after admission to construct an early diagnosis and prediction model for severe acute pancreatitis.The task of feature screening and hyperparameter optimization was completed simultaneously.The ReliefF algorithm feature importance rank and multivariate logistic analysis were used to analyze the value of the selected features.Results In the training set,the area under curve(AUC)of the improved machine learning model was 0.922.In the testing set,the AUC of the improved machine learning model reached 0.888.The 4 key features of predicting severe acute pancreatitis based on the improved Archimedes optimization algorithm were C-reactive protein,blood chlorine,blood magnesium and fibrinogen level,which were consistent with the results of ReliefF algorithm feature importance ranking and multivariate logistic analysis.Conclusion The application of improved machine learning model analyzing the laboratory examination results can help to early predict the occurrence of severe acute pancreatitis.

Purpose: The risk stratification of pediatric anaplastic large cell lymphoma (ALCL) has not been standardized. In this study, new risk factors were included to establish a new risk stratification system for ALCL, and its feasibility in clinical practice was explored. Materials and Methods: On the basis of the non-Hodgkin’s lymphoma Berlin–Frankfurt–Munster 95 (NHL-BFM-95) protocol, patients with minimal disseminated disease (MDD), high-risk tumor site (multiple bone, skin, liver, and lung involvement), and small cell/lymphohistiocytic (SC/LH) pathological subtype were enrolled in risk stratification. Patients were treated with a modified NHL-BFM-95 protocol combined with an anaplastic lymphoma kinase inhibitor or vinblastine (VBL). Results: A total of 136 patients were enrolled in this study. The median age was 8.8 years. The 3-year event-free survival (EFS) and overall survival of the entire cohort were 77.7% (95% confidence interval [CI], 69.0% to 83.9%) and 92.3% (95% CI, 86.1% to 95.8%), respectively. The 3-year EFS rates of low-risk group (R1), intermediate-risk group (R2), and high-risk group (R3) patients were 100%, 89.5% (95% CI, 76.5% to 95.5%), and 67.9% (95% CI, 55.4% to 77.6%), respectively. The prognosis of patients with MDD (+), stage IV cancer, SC/LH lymphoma, and high-risk sites was poor, and the 3-year EFS rates were 45.3% (95% CI, 68.6% to 19.0%), 65.7% (95% CI, 47.6% to 78.9%), 55.7% (95% CI, 26.2% to 77.5%), and 70.7% (95% CI, 48.6% to 84.6%), respectively. At the end of follow-up, one of the five patients who received maintenance therapy with VBL relapsed, and seven patients receiving anaplastic lymphoma kinase inhibitor maintenance therapy did not experience relapse. Conclusion This study has confirmed the poor prognostic of MDD (+), high-risk site and SC/LH, but patients with SC/LH lymphoma and MDD (+) at diagnosis still need to receive better treatment (ClinicalTrials.gov number, NCT03971305).