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 effects of combining APR-246 with irradiation for enhancing anti-tumor immune response against 4T1 breast cancer cells, and to develop multiple tumor treatment strategies.Methods:The control group, APR-246 group, irradiation group and irradiation combined APR-246 group were used both in the cell experiment and tumor-bearing mice experiment. The inhibitory effect of APR-246 on the proliferation of 4T1 cells was assessed by using Cell Counting Kit-8. The effect of APR-246 with irradiation on the survival rate of 4T1 cells using clone formation assay was measured. The levels of reactive oxygen species (ROS) and lipid peroxidation (LPO) in tumor cells using a 2’, 7’-dichlorodihydrofluorescein diacetate (DCFH-DA) fluorescent probe and a lipid peroxidation sensor, the tumor inhibition rates of different groups of tumor bearing mice were compared, and the proportions of CD4+ and CD8+ T cells and the ratio of M1/M2 macrophages were determined in the tumor microenvironment by flow cytometry.Results:Compared with irradiation group, 2, 4, 6 Gy irradiation combined APR-246 group significantly reduced the survival rates of 4T1 cells ( t = 2.89, 4.15, 2.62, P < 0.05), the 6 Gy irradiation combined APR-246 group significantly increased the levels of ROS ( t = 16.95, P < 0.05) and LPO ( t = 6.09, P < 0.05) in 4T1 cells, and significantly increased the apoptosis rate of 4T1 cells ( t = 10.99, P < 0.05). Meanwhile, from the 16 th day of tumor inoculation, the 10 Gy irradiation combined APR-246 group showed significantly inhibited tumor growth ( t = 2.38-2.91, P < 0.05) and significantly increased proportions of CD4+ and CD8+ T cells ( t = 9.96, 6.28, P < 0.05) and M1/M2 ratio ( t = 15.30, P < 0.05) in tumor tissues. Conclusions:APR-246 combined with irradiation can effectively increase ROS and LPO levels in 4T1 cells, promote tumor cell apoptosis, and induce anti-tumor immune response, thus potentially inhibiting the growth of 4T1 cells.

Objective:To explore the protective effects and mechanisms of an indole-3-acetaldehyde (I3A)-loaded inulin-based hydrogel against radiation-induced intestinal injury.Methods:The gelation properties and injectability of the I3A-loaded inulin-based hydrogel were detected using a rheometer, and its biocompatibility was assessed via a CCK-8 assay. Eighteen C57BL/6 mice (aged: 6-8 weeks) were stratified by body weight and randomly assigned into three groups with 6 mice in each group: blank control, irradiation-only, and irradiation+ hydrogel protection. Abdominal irradiation was administered using 137Cs γ-rays at 17 Gy. The irradiation+ hydrogel protection group received 200 μl/day of I3A-loaded inulin-based hydrogel for two days before and 2-3 days after irradiation. Meanwhile, the irradiation-only group was treated with an equivalent volume of sterile water via gavage. The mice were euthanized four days post-irradiation, and their intestinal tissues were harvested. Hematoxylin-eosin (HE) staining, Ki67 immunohistochemistry, and TUNEL immunofluorescence were performed to assess histopathological damage, epithelial cell proliferation, and apoptosis, respectively. Quantitative real-time PCR (qRT-PCR) was employed to measure mRNA levels of inflammatory and antioxidant factors. Gut microbiota composition was analyzed via 16S rRNA sequencing. Results:The test results of the rheometer confirmed successful hydrogel formation. CCK-8 assays demonstrated excellent biocompatibility. Compared with the irradiation-only group, the irradiation+ hydrogel protection group exhibited preserved intestinal histoarchitecture, a 1.5-fold increase in intestinal cell proliferation ( t = 8.35, P < 0.05), and a 2-fold reduction in radiation-induced apoptosis ( t = 7.94, P < 0.05). Moreover, the hydrogel group showed significantly elevated expression of the anti-inflammatory cytokine IL-10 and antioxidant factors NRF-2 and HO-1 ( t = 3.16, 24.83, 5.92, P < 0.05), alongside reduced levels of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α ( t = 5.15, 3.82, 3.83, P < 0.05). Gut microbiota analysis revealed significant modulation in microbial composition and abundance in the hydrogel group. Conclusions:The I3A-loaded inulin-based hydrogel can significantly promote intestinal cell proliferation, reduce radiation-induced apoptosis, and enhance both anti-inflammatory and antioxidant responses. In addition, it regulates gut microbiota composition and abundance, protecting against radiation-induced intestinal injury.

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 application effects of a four-in-one blended teaching model integrating artificial intelligence, virtual reality, 3D printing, and case-based learning (CBL) in the clinical teaching of spinal tumors.Methods:We divided 89 students on training in the Department of Orthopedics of Peking University Third Hospital from September 2022 to August 2024 into control group ( n=47) and experimental group ( n=42). The control group adopted traditional teaching, and the experimental group adopted the four-in-one teaching model. At the end of clinical teaching, an artificial intelligence test and a questionnaire survey were administered to the students to evaluate the teaching effects. The two groups were compared using the independent samples t-test with the use of SPSS 27.0. Results:The experimental group was superior to the control group with significant improvements in the answer accuracy rate (66.67%, χ2=9.44, P=0.002), learning interest [(4.50±0.63), t=2.75, P=0.007], theoretical knowledge mastery [(4.64±0.69), t=7.74, P<0.001], clinical thinking [(4.48±0.71), t=9.08, P<0.001], practical skills [(4.13±0.89), t=2.69, P=0.009], scientific research innovation [(4.71±0.59), t=9.28, P<0.001], teacher-student interaction [(4.74±0.54), t=12.76, P<0.001], and classroom attention [(4.69±0.52), t=12.64, P<0.001]. At the same time, the students in the experimental group put forward numerous constructive feedback. Conclusions:The four-in-one blended teaching model combining artificial intelligence, virtual reality, 3D printing, and CBL can help undergraduate medical students better recognize and diagnose spinal tumors with a correct clinical thinking path, achieving good teaching effects.

Objective:To explore the application effects of a four-in-one blended teaching model integrating artificial intelligence, virtual reality, 3D printing, and case-based learning (CBL) in the clinical teaching of spinal tumors.Methods:We divided 89 students on training in the Department of Orthopedics of Peking University Third Hospital from September 2022 to August 2024 into control group ( n=47) and experimental group ( n=42). The control group adopted traditional teaching, and the experimental group adopted the four-in-one teaching model. At the end of clinical teaching, an artificial intelligence test and a questionnaire survey were administered to the students to evaluate the teaching effects. The two groups were compared using the independent samples t-test with the use of SPSS 27.0. Results:The experimental group was superior to the control group with significant improvements in the answer accuracy rate (66.67%, χ2=9.44, P=0.002), learning interest [(4.50±0.63), t=2.75, P=0.007], theoretical knowledge mastery [(4.64±0.69), t=7.74, P<0.001], clinical thinking [(4.48±0.71), t=9.08, P<0.001], practical skills [(4.13±0.89), t=2.69, P=0.009], scientific research innovation [(4.71±0.59), t=9.28, P<0.001], teacher-student interaction [(4.74±0.54), t=12.76, P<0.001], and classroom attention [(4.69±0.52), t=12.64, P<0.001]. At the same time, the students in the experimental group put forward numerous constructive feedback. Conclusions:The four-in-one blended teaching model combining artificial intelligence, virtual reality, 3D printing, and CBL can help undergraduate medical students better recognize and diagnose spinal tumors with a correct clinical thinking path, achieving good teaching effects.

Objective:To explore the effects of combining APR-246 with irradiation for enhancing anti-tumor immune response against 4T1 breast cancer cells, and to develop multiple tumor treatment strategies.Methods:The control group, APR-246 group, irradiation group and irradiation combined APR-246 group were used both in the cell experiment and tumor-bearing mice experiment. The inhibitory effect of APR-246 on the proliferation of 4T1 cells was assessed by using Cell Counting Kit-8. The effect of APR-246 with irradiation on the survival rate of 4T1 cells using clone formation assay was measured. The levels of reactive oxygen species (ROS) and lipid peroxidation (LPO) in tumor cells using a 2’, 7’-dichlorodihydrofluorescein diacetate (DCFH-DA) fluorescent probe and a lipid peroxidation sensor, the tumor inhibition rates of different groups of tumor bearing mice were compared, and the proportions of CD4+ and CD8+ T cells and the ratio of M1/M2 macrophages were determined in the tumor microenvironment by flow cytometry.Results:Compared with irradiation group, 2, 4, 6 Gy irradiation combined APR-246 group significantly reduced the survival rates of 4T1 cells ( t = 2.89, 4.15, 2.62, P < 0.05), the 6 Gy irradiation combined APR-246 group significantly increased the levels of ROS ( t = 16.95, P < 0.05) and LPO ( t = 6.09, P < 0.05) in 4T1 cells, and significantly increased the apoptosis rate of 4T1 cells ( t = 10.99, P < 0.05). Meanwhile, from the 16 th day of tumor inoculation, the 10 Gy irradiation combined APR-246 group showed significantly inhibited tumor growth ( t = 2.38-2.91, P < 0.05) and significantly increased proportions of CD4+ and CD8+ T cells ( t = 9.96, 6.28, P < 0.05) and M1/M2 ratio ( t = 15.30, P < 0.05) in tumor tissues. Conclusions:APR-246 combined with irradiation can effectively increase ROS and LPO levels in 4T1 cells, promote tumor cell apoptosis, and induce anti-tumor immune response, thus potentially inhibiting the growth of 4T1 cells.