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.

Inflammaging is a process of cellular dysfunction associated with chronic inflammation, which plays a significant role in the onset and progression of liver diseases. Research on its mechanisms has become a hotspot. In viral hepatitis, inflammaging primarily involve oxidative stress, cell apoptosis and necrosis, as well as gut microbiota dysbiosis. In non-alcoholic fatty liver disease, inflammaging is more complex, involving insulin resistance, fat deposition, lipid metabolism disorders, gut microbiota dysbiosis, and abnormalities in NAD⁺ metabolism. In liver tumors, inflammaging is characterized by weakening of tumor suppressive mechanisms, remodeling of the liver microenvironment, metabolic reprogramming, and enhanced immune evasion. Therapeutic strategies targeting inflammaging have been developing recently, and antioxidant therapy, metabolic disorder improvement, and immunotherapy are emerging as important interventions for liver diseases. This review focuses on the mechanisms of inflammaging in liver diseases, aiming to provide novel insights for the prevention and treatment of liver diseases.
Humans ; Liver Diseases/pathology* ; Inflammation ; Oxidative Stress ; Non-alcoholic Fatty Liver Disease ; Liver Neoplasms ; Gastrointestinal Microbiome

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.

Recent studies have demonstrated that the malignant proliferation and continuous progression of lymphoma are highly dependent on enhanced lipid metabolism. Consequently, targeting lipid metabolism pathways have emerged as a promising direction for lymphoma treatment. This paper reviews the fatty acid metabolism, cholesterol metabolism, and phospholipid metabolism in lymphoma, thereby providing novel insights for its therapeutic strategies.

Objective To evaluate the clinical value of non-invasive ultrasound hemodynamic monito-ring in critically ill neonates in plateau area(altitude＞2 500 m).Methods A total of 205 critically ill neo-nates admitted to the NICU of Qamdo People's Hospital from September 2023 to December 2024 were ran-domly divided into the control group(n=105)and the observation group(n=100)using a random number table.There was no statistically significant difference in the baseline data between the two groups(P＞0.05).The observation group underwent noninvasive ultrasonic hemodynamic monitoring within 12 hours of admis-sion,measuring corrected flow time(FTC),stroke volume(SV),cardiac index,systemic vascular resistance index(SVRI),and myocardial contractility index(SMII).The control group received conventional monito-ring.The treatment was adjusted based on the results.The above parameters were remeasured 4-8 hours af-ter treatment adjustment.Hospital stay and 28 d survival rate were compared between the two groups.Multi-ple linear regression and binary logistic regression were used to analyze the factors influencing hospital stay and 28 d survival rate,respectively.Results Compared to pre-treatment levels,the observation group showed significant post-treatment decreases in FTC[(364.03±47.70)ms vs.(414.09±52.20)ms]and SVRI[(1 521.00±186.93)dyn·s·(cm5)-1·(m2)-1 vs.(1 720.00±347.31)dyn·s·(cm5)-1·(m2)-1].Con-versely,significant increases were observed in SV[(5.23±0.66)mL vs.(5.09±0.88)mL],cardiac index[(3.35±1.17)L·min-1·(m2)-1 vs.(3.19±0.99)L·min-1·(m2)-1],and SMII[(0.56±0.10)W/m2 vs.(0.51±0.14)W/m2](P＜0.05).The observation group had a significantly shorter hospital stay than the control group[(11.53±3.61)d vs.(13.83±2.56)d,P＜0.05].The 28 d survival rate was higher in the ob-servation group(100.0％vs.98.1％),although the difference was not statistically significant(P＞0.05).Multiple linear regression analysis revealed that both gestational age and cardiac index were negatively correla-ted with the duration of hospital stay.Binary logistic regression analysis indicated that birth weight was a sig-nificant factor influencing the 28 d survival rate(OR=4.600,95％CI:2.465-10.654,P=0.001).Conclusion Non-invasive ultrasonic hemodynamic monitoring can facilitate early identification of hemodynamic alterations in critically ill neonates in platean area,which could guide treatment and potentially reducing hospital stay.