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.

In recent years, China has been facing the dual challenges of declining fertility rates and births, with male reproductive health issues, especially the decline in semen quality, identified as a pivotal contributor to this phenomenon. Meanwhile, accumulating evidence indicates that air pollutants, an increasingly severe environmental problem, can damage semen quality not only directly through their biological toxicity but also indirectly by disrupting the composition of microbial communities in the gut and semen, thereby dysregulating immune function, endocrine homeostasis, and oxidative stress responses. The gut microbiota and semen microbiota, as important components of the human microecosystem, play crucial roles in maintaining reproductive health. This article comprehensively reviewed the research progress on the potential effects of air pollutants (particulate matter and gaseous pollutants), gut microbiota, and semen microbiota on semen quality. Specifically, it elucidated the mechanisms of interaction between these factors and explored how they affect male fertility.

Cancer multidrug resistance (MDR) impairs the therapeutic efficacy of various chemotherapeutics. Novel approaches, particularly the development of MDR reversal agents, are critically needed to address this challenge. This study demonstrates that tenacissoside I (TI), a compound isolated from Marsdenia tenacissima (Roxb.) Wight et Arn, traditionally used in clinical practice as an ethnic medicine for cancer treatment, exhibits significant MDR reversal effects in ABCB1-mediated MDR cancer cells. TI reversed the resistance of SW620/AD300 and KBV200 cells to doxorubicin (DOX) and paclitaxel (PAC) by downregulating ABCB1 expression and reducing ABCB1 drug transport function. Mechanistically, protein arginine methyltransferase 1 (PRMT1), whose expression correlates with poor prognosis and shows positive association with both ABCB1 and EGFR expressions in tumor tissues, was differentially expressed in TI-treated SW620/AD300 cells. SW620/AD300 and KBV200 cells exhibited elevated levels of EGFR asymmetric dimethylarginine (aDMA) and enhanced PRMT1-EGFR interaction compared to their parental cells. Moreover, TI-induced PRMT1 downregulation impaired PRMT1-mediated aDMA of EGFR, PRMT1-EGFR interaction, and EGFR downstream signaling in SW620/AD300 and KBV200 cells. These effects were significantly reversed by PRMT1 overexpression. Additionally, TI demonstrated resistance reversal to PAC in xenograft models without detectable toxicities. This study establishes TI's MDR reversal effect in ABCB1-mediated MDR human cancer cells through inhibition of PRMT1-mediated aDMA of EGFR, suggesting TI's potential as an MDR modulator for improving chemotherapy outcomes.
Humans ; Protein-Arginine N-Methyltransferases/antagonists & inhibitors* ; Drug Resistance, Neoplasm/drug effects* ; ErbB Receptors/genetics* ; Animals ; Cell Line, Tumor ; Drug Resistance, Multiple/drug effects* ; Methylation/drug effects* ; Saponins/administration & dosage* ; Mice ; Mice, Nude ; Mice, Inbred BALB C ; ATP Binding Cassette Transporter, Subfamily B/genetics* ; Doxorubicin/pharmacology* ; Paclitaxel/pharmacology* ; Female ; Repressor Proteins

Objective:To explore the mediating effect of appearance functional internalization between social comparison tendency and body image distress of college students.Methods:From June to September 2023, a cross-sectional survey was conducted among 308 college students with the social comparison tendency scale, the functional internalization questionnaire of college students' appearance and the body image anxiety scale for adolescent students.SPSS 22.0 software was used for common method bias test, descriptive statistics and correlation analysis, Mplus 8.0 was used for structural equation construction, and Bootstrap method was used for intermediary effect analysis.Results:Body image distress (43.86±8.78) was positively correlated with social comparison tendency (35.20±6.04) and appearance functional internalization (38.35±9.68) ( r=0.35, 0.33, both P<0.01).There was also a positive correlation between social comparison tendency and appearance functional internalization ( r=0.51, P<0.01). The effect of social comparison tendency on body image distress was mediated by appearance functional internalization, and the mediating effect value was 0.137, accounted for 35.13%(0.137/0.390) of the total effect. Conclusion:The appearance functional internalization mediates the relationship between social comparison tendency and body image distress.Social comparison tendency can not only directly predict body image distress, but also can predict body image distress, indirectly through appearance functional internalization.

Objective:To explore the relationship between social exclusion, rumination, executive function and mobile phone addiction among college students.Methods:From November to December 2023, a total of 516 college students were investigated by social exclusion questionnaire for undergraduate, ruminative responses scale, the Geurten-questionnaire of executive functioning in Chinese college students and mobile phone addiction tendency scale. SPSS 26.0 statistical software was used for common method bias test, descriptive statistics, correlation analysis, and PROCESS 3.5 macro program was used to test the mediation effect.Results:Social exclusion (31.21±12.69), rumination (42.85±12.38), executive function (71.46±9.41), and college students' mobile phone addiction tendency (43.53±11.74) were all significantly and positively correlated with each other ( r=0.299-0.500, all P<0.01). The direct effect of social exclusion on mobile phone addiction was significant (effect size=0.138, 95% CI=0.048-0.228), accounting for 37.91%(0.138/0.364) of the total effect. Rumination had a mediating effect between social exclusion and mobile phone addiction (effect size=0.053, 95% CI=0.001-0.112), accounting for 14.56%(0.053/0.364) of the total effect. Executive function mediates the relationship between social exclusion and mobile phone addiction (effect size=0.137, 95% CI=0.091-0.188), accounting for 37.64%(0.137/0.364) of the total effect.Rumination and executive function has a chain mediating effect between social exclusion and mobile phone addiction (effect size=0.036, 95% CI=0.016-0.061), accounting for 9.89%(0.036/0.364) of the total effect. Conclusion:Social exclusion can directly affect mobile phone addiction in college students and can also influence mobile phone addiction in college students through the independent mediating effects of rumination and executive function, as well as the chain mediating effect of rumination and executive function.

Objective:To evaluate the accuracy of an artificial intelligence (AI) model in measuring key angles on pelvic radiographs of the hip and assess its effectiveness in diagnosing developmental dysplasia of the hip (DDH) and borderline developmental dysplasia of the hip (BDDH).Methods:A retrospective analysis was conducted using anteroposterior pelvic X-ray films from 1,029 patients with suspected DDH. The data were collected from the Department of Radiology, Fourth Medical Center of the Chinese PLA General Hospital. Among the patients, 273 were male, and 756 were female, with an average age of 57.01 ± 18.16 years (range, 12-88 years). The dataset was randomly divided into a training set (720 cases), a test set (206 cases), and a validation set (103 cases). Two radiologists identified and marked key anatomical points of the hip joint to establish the training dataset, which was then used to develop a deep learning-based AI model capable of locating these key anatomical positions. Using the identified anatomical points, the AI model automatically measured and calculated the Sharp angle, center-edge (CE) angle, and T?nnis angle in the test dataset. The measurement results from the AI model were compared with those of the radiologists to evaluate the model's accuracy. The validation set was used to optimize model parameters, and the test dataset was used to evaluate the diagnostic performance of DDH. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic efficacy of the AI model for DDH and BDDH.Results:The accuracy rates of the AI model in measuring the left Sharp angle, CE angle, and T?nnis angle for diagnosing DDH were 89.8%, 90.1%, and 86.8%, respectively. For the right side, the accuracy rates were 93.7%, 92.2%, and 80.5%, respectively. There were no statistically significant differences in the mean values of the Sharp, T?nnis, and CE angles between manual and AI measurements ( P>0.05). Pearson correlation tests and intraclass correlation coefficient (ICC) analyses revealed high consistency between AI and manual measurements of the Sharp angle, T?nnis angle, and CE angle, with r-values and ICC values exceeding 0.75. Additionally, the AI model performed measurements significantly faster (1.7±0.1 s) than radiologists (88.1±8.4 s and 90.3±7.4 s, P<0.001). The areas under the ROC curves (AUCs) for diagnosing DDH using the Sharp angle, CE angle, and T?nnis angle measured by the AI model were 0.883, 0.922, and 0.908 (left side) and 0.924, 0.871, and 0.922 (right side), respectively. For diagnosing BDDH, the AUCs of the left and right CE angles measured by the AI model were 0.787 and 0.676, respectively. Kappa test results indicated good agreement between the AI model and manual measurements as well as final clinical diagnoses. For the CE angle, the κ value of the AI model was 0.663, while κ values for the Sharp and T?nnis angles were all greater than 0.800. Conclusion:The convolutional neural network-based AI model effectively and automatically measures the Sharp, CE, and T?nnis angles and demonstrates high diagnostic efficacy for DDH and BDDH.