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.

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:To investigate the prognosis and the factors that influence it in patients with non-gastric gastrointestinal stromal tumors (GISTs) who are at low risk of recurrence.Methods:This was a retrospective cohort study. Clinicopathologic and prognostic data from patients with non-gastric GISTs and at low risk of recurrence (i.e., very low-risk or low-risk according to the 2008 version of the Modified NIH Risk Classification), who attended 18 medical centers in China between January 2000 and June 2023, were collected. We excluded patients with a history of prior malignancy, concurrent primary malignancy, multiple GISTs, and those who had received preoperative imatinib. The study cohort comprised 1,571 patients with GISTs, 370 (23.6%) of whom were at very low-risk and 1,201 (76.4%) at low-risk of recurrence. The cohort included 799 (50.9%) men and 772 (49.1%) women of median age 57 (16–93) years. Patients were followed up to July 2024. The prognosis and its influencing factors were analyzed. Receiver operating characteristic curves for tumor diameter and Ki67 were established, and the sensitivity, specificity, area under the curve (AUC) and optimal cut-off value with 95% confidence intervals were calculated. Propensity score matching was implemented using the 1:1 nearest neighbor matching method with a matching tolerance of 0.02.Results:With a median follow-up of 63 (12–267) months, the 5- and 10-year overall survival (OS) rates of the 1,571 patients were 99.5% and 98.0%, respectively, and the 5- and 10-year disease-free survival (DFS) rates were 96.3% and 94.4%, respectively. During postoperative follow-up, 3.8% (60/1,571) patients had disease recurrence or metastasis, comprising 0.8% (3/370) in the very low-risk group and 4.7% (57/1,201) in the low-risk group. In the low-risk group, recurrence or metastasis occurred in 5.5% (25/457) of patients with duodenal GISTs, 3.9% (25/645) of those with small intestinal GISTs, 9.2% (6/65) of those with rectal GISTs, and 10.0% (1/10) of those with colonic GISTs. Among the 60 patients with metastases, 56.7% (34/60) of the metastases were located in the abdominal cavity, 53.3% (32/60) in the liver, and 3.3% (2/60) in bone. During the follow-up period, 13 patients (0.8%) died of disease. Receiver operating characteristic curves were plotted for tumor diameter and Ki67 and assessed using the Jordon index. This showed that the difference in DFS between the two groups was statistically significant when the cutoff value for tumor diameter was 3.5 cm (AUC 0.731, 95% CI: 0.670–0.793, sensitivity 77.7%, specificity 64.1%). Furthermore, the difference in DFS between the two groups was statistically significant when the cutoff value for Ki67 was 5% (AUC 0.693, 95% CI: 0.624–0.762, sensitivity 60.7%, specificity 65.3%). Multifactorial analysis revealed that tumor diameter ≥3.5 cm, Ki67 ≥5%, and R1 resection were independent risk factors for DFS in patients with non-gastric GISTs at low risk of recurrence (all P<0.05). Furthermore, age >57 years, Ki67 ≥5%, and R1 resection were also independent risk factors for OS in patients with non-gastric GISTs at low risk of recurrence (all P<0.05). We also grouped the patients according to whether they had received postoperative adjuvant treatment with imatinib for 1 or 3 years. This yielded 137 patients in the less than 1-year group, 139 in the 1-year plus group; and 44 in both the less than 3 years and 3-years plus group. After propensity score matching for age, tumor diameter, Ki67, and resection status, the differences in survival between the two groups were not statistically significant (all P>0.05). The 10-year DFS and OS were 87.5% and 95.5%, respectively, in the group treated with imatinib for less than 1 year and 88.5% and 97.8%, respectively, in the group treated for more than 1 year. The 10-year DFS and OS were 89.6% and 92.6%, respectively, in the group treated with imatinib for less than 3 years and 88.0% and 100.0%, respectively, in the group treated with imatinib for more than 3 years. Conclusion:The overall prognosis of primary, non-gastric, low recurrence risk GISTs is relatively favorable; however, recurrences and metastases do occur. Age, tumor diameter, Ki67, and R1 resection may affect the prognosis. For some patients with low risk GISTs, administration of adjuvant therapy with imatinib for an appropriate duration may help prevent recurrence and improve survival.

Objective:To explore the effect of sleep deprivation on hair growth cycle in mice.Methods:Seventy-two adult C57BL/6J mice were randomly divided into a control group of 36 mice and an experimental group of 36 mice using a lottery method. After hair removal, in the control the group the mice were subjected to routine feeding in a horizontal platform environment for 16 hours per day, while in the experimental group the mice were subjected to sleep deprivation using an improved water platform for 16 hours per day. On the 1st, 9th, and 19th day after the experiment, the skin tissue from the hair removal area of the two groups of mice was taken for HE staining. Then the mouse hair was staged and scored by observing hair follicle morphology under an optical microscope. The cycle and score of the hair was started from the telogen, which was set as 0 points; the anagen Ⅰ to V were set as 1-5 points; the anagen Ⅵ and catagen Ⅰ were set as 6 points; the catagen Ⅱ to Ⅷ were set at 7-13 points. Statistical analysis was conducted by SPSS 26.0. The hair cycle score was expressed as Mean±SD. Student’s t-test was used for comparison between groups at the same time point, and Welch-test was used for comparison of scores at different time points within the group. P<0.05 indicated a statistically significant difference. Results:On the 1st, 9th, and 19th day, the hair cycle scores of the control group were 1.00 ± 0.57, 5.04 ± 0.94, and 9.52 ± 0.87 points, while the hair cycle scores of the experimental group were 0.85 ± 0.62, 2.40 ± 0.50 and 6.08 ± 0.42 points. Inter group comparison showed that the hair cycle scores of the experimental group were lower than those of the control group at all three time points. There was no statistically significant difference on the 1st day ( t=1.03, P=0.307), while the differences were statistically significant on the 9th day ( t=13.38, P<0.001) and the 19th day ( t=16.41, P<0.001). Intragroup comparison showed that there was a statistically significant difference in hair cycle scores between the control group and the experimental group at different time points( P<0.01), and the scores increased over time. Conclusion:Sleep deprivation can cause a lag in the hair growth cycle of mice, but it does not cause a cessation of the hair growth cycle. The degree of lag gradually increases with the number of deprivation days.