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 diagnosis, treatment and prognosis of primary prostatic signet ring cell carcinoma (SRCC), so as to provide reference for the clinical diagnosis and treatment. Methods: A retrospective analysis was conducted on the clinical data of 6 patients with primary prostatic SRCC treated in Nanjing Drum Tower Hospital during Nov.2020 and Sep.2024.The clinical manifestations, imaging features, treatment methods, histological characteristics and prognosis were summarized. Results: The average age of the patients was (72.00±4.28) years.Varying degrees of dysuria occurred in 4 patients. All patients underwent multi-parametric magnetic resonance imaging (mpMRI) examination before surgery, and the results indicated typical prostate cancer.Preoperative biopsies showed high-grade (Gleason 8-10) prostate acinar adenocarcinoma.Postoperative pathological diagnoses were mixed types of prostate acinar adenocarcinoma and SRCC, and no metastasis was found in the pelvic lymph nodes.All patients were followed up for 1 to 46 months after surgery and are currently alive.Robot-assisted laparoscopic radical prostatectomy only was performed in 3 cases; apalutamide and leuprolide/triptorelin was administered after surgery in 2 cases; bicalutamide + goserelin was administered after surgery in 1 case, who developed bladder metastasis of prostate cancer 24 months later, and the serum prostate-specific antigen (PSA) concentration decreased to a safe level (<0.2 ng/mL) after the use of darolutamide with radiotherapy.No recurrence or metastasis was found in the remaining patients. Conclusion: Primary prostatic SRCC is a rare and highly aggressive malignant tumor of the prostate.The diagnosis depends on pathological examinations due to lack of specific imaging features and clinical manifestations.The prognosis is poor, and there is currently no standardized treatment.The combined use of surgery, hormonotherapy and radiotherapy can help improve the survival rate of patients.

The cellular and molecular components of the tumor immune microenvironment (TIME) and their information exchange processes significantly influence the trends of anti-tumor immunity. In recent years, numerous studies have begun to evaluate TIME in the context of previous cancer treatment strategies. This review will systematically summarize the compositional characteristics of TIME and, based on this foundation, explore the impact of current cancer therapies on the remodeling of TIME, aiming to provide new insights for the development of innovative immune combination therapies that can convert TIME into an anti-tumor profile.
Tumor Microenvironment/immunology* ; Humans ; Neoplasms/therapy* ; Immunotherapy/methods* ; Animals

Objective To investigate prokaryotic expression of the antigen sequence (amino acids 59-145) of mouse leukemia-related protein 16 (LRP16) protein and preparation of rabbit anti-mouse LRP16 polyclonal antibody. Methods The prokaryotic expression plasmid pLS962-LRP16 was constructed by the molecular cloning method and transferred into E.coli Rosetta to express LRP16 protein induced by IPTG. The recombinant protein was purified using Ni-NTA affinity columns followed by gel filtration chromatography. New Zealand white rabbits were immunized with the purified antigen to generate polyclonal antiserum, with antibody titer quantified by ELISA. Antigen-specific IgG was affinity-purified using Sepharose-coupled LRP16 and validated through Western blot and immunofluorescence assays. Results SDS-PAGE analysis confirmed insoluble expression of the LRP16 fusion protein as inclusion bodies. ELISA demonstrated exceptional antiserum titer (1:256 000). Western blot and immunofluorescence verified that the polyclonal antibody could specifically recognize endogenous LRP16 in murine tissues. Conclusion The prokaryotic expression of the LRP16 gene is successfully achieved, and the rabbit anti-mouse LRP16 polyclonal antibody exhibiting high specificity is prepared. This lays the foundation for further studies on the function of the LRP16 gene.
Animals ; Rabbits ; Mice ; Antibodies/immunology* ; Escherichia coli/metabolism* ; Enzyme-Linked Immunosorbent Assay ; Blotting, Western ; Antibody Specificity

Objective: To explore the relationship between processed food consumption and blood pressure level of students in a university in Yunnan Province, so as to provide the reference for preventing hypertension in university students. Methods: In October 2021, a cluster sampling method was used to select 4 781 freshmen from a university in Kunming, Yunnan Province. The frequency of processed food consumption of university students was assessed by using the dietary frequency questionnaire, and height, weight and blood pressure were measured. Mann-Whitney test and Kruskal-Wallis test were used to compare the differences in blood pressure level of university students with different demographic variables, and the association between processed food consumption and blood pressure level was analyzed with a generalized linear model. Results: Among the students of a university in Yunnan Province, the detection rates of systolic prehypertension and hypertension were 33.86% and 1.23%, and the detection rates of diastolic prehypertension were 32.13% and hypertension 7.22%. The results of generalized linear model analysis showed that after controlling for demographic variables and other variables that might affect the blood pressure level of university students, the consumption of processed food (bread and cake: β =0.15, 95% CI =0.01-0.29) and ultra processed food (coffee beverage: β =-0.29, 95% CI =-0.54--0.03) were associated with systolic blood pressure level( P <0.05). The consumption of processed food (salted duck egg: β =0.21, 95% CI =0.01-0.41) was correlated with the diastolic blood pressure of college students ( P <0.05). Conclusions Processed food consumption in university students may increase the risk of high blood pressure.The education of healthy eating among college students should be strengthened to reduce the consumption of processed foods.