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 a nomogram of intratumor and peritumor radiomics based on enhanced CT venous phase to pre-operatively predict the pathological grade of poorly differentiated esophageal squamous cell carcinoma(ESCC).Methods A retro-spective selection was made of 266 ESCC patients confirmed by pathology(76 cases of poorly differentiated;190 cases of non-poorly differentiated),and all patients were randomly divided into training set(n=186),validation set(n=80),and full data set(n=266).Tumors were segmented on the enhanced CT venous phase to create three-dimensional region of interest(ROI)of intratumor,peritu-mor 0.3 cm,and intratumor+peritumor 0.3 cm.A total of 2 553 radiomics features were extracted.After feature dimensionality reduc-tion,XGboost machine learning algorithm was utilized to rank the top fifteen features.Stepwise forward multiple logistic regression was employed to identify the most significant features.The radiomics scores of the intratumor,peritumor 0.3 cm,and intratumor+peritu-mor 0.3 cm were calculated.The diagnostic efficacy of the model was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results The nomogram constructed based on radiomics scores of intratumor,peritumor 0.3 cm,intratumor+peritumor 0.3 cm in the training set for preoperative prediction of poorly differentiated ESCC had an AUC of 0.899[95％confidence interval(CI)0.846-0.938],and it was well validated in the vali-dation set(AUC 0.869,95％CI 0.775-0.934)and the full data set(AUC 0.889,95％CI 0.845-0.924).Additionally,calibration curves and DCA indicated that the nomogram achieved good calibration ability in the three cohorts and offered greater clinical net benefit.Conclusion The nomogram based on enhanced CT venous phase intratumor and peritumor radiomics achieves a high and stable diagnostic efficacy for preoperatively predicting poorly differentiated ESCC,which may help with individualized surgical selec-tion and management before surgery.

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 investigate the distribution characteristics and related factors of weight loss behavior among middle school students in Shanghai, so as to provide a reference for guiding scientific weight loss among middle school students. Methods: From May to June 2021, a stratified cluster random sampling method was used to select 16 758 junior and high school students in 16 districts of Shanghai. Youth Risk Behavior Surveillance System was administered to assess the basic condition and weight loss behaviors of the students. An unordered multinomial Logistic regression model was employed to analyze the factors associated with weight loss behaviors. Results: A total of 5 881 (35.09%) reported engaging in exercise for weight loss, 6 344 (37.86%) reported dieting for weight loss, and 461 (2.75%) engaged in unhealthy weight loss behaviors. The unordered multinomial Logistic regression analysis indicated that compared with the no weight loss behavior group, students from urban areas( OR =1.35,95% CI =1.10-1.66), those with Internet addiction ( OR =1.71,95% CI =1.23-2.38), those with victims of bullying ( OR =2.09, 95% CI =1.68-2.61), those experiencing insomnia ( OR =2.33,95% CI = 1.74-3.11), those feelings of sadness or despair ( OR =3.10, 95% CI =2.42- 3.97 ), and those who perceived their body weight as slightly heavy ( OR =2.77, 95% CI = 2.17-3.55) or very heavy ( OR =3.41, 95% CI =2.44-4.75) were more likely to engage in unhealthy weight loss behaviors ( P <0.05). Conclusions There are significant differences in weight loss behaviors among middle school students with varying characteristics in Shanghai. Negative emotions such as insomnia and feelings of sadness or despair, Internet addiction, cognitive bias in weight and experiences of bullying are identified as related factors for unhealthy weight loss behaviors. Targeted intervention measures should be implemented to guide students towards scientific approaches to weight management.

Objective To explore a nomogram of intratumor and peritumor radiomics based on enhanced CT venous phase to pre-operatively predict the pathological grade of poorly differentiated esophageal squamous cell carcinoma(ESCC).Methods A retro-spective selection was made of 266 ESCC patients confirmed by pathology(76 cases of poorly differentiated;190 cases of non-poorly differentiated),and all patients were randomly divided into training set(n=186),validation set(n=80),and full data set(n=266).Tumors were segmented on the enhanced CT venous phase to create three-dimensional region of interest(ROI)of intratumor,peritu-mor 0.3 cm,and intratumor+peritumor 0.3 cm.A total of 2 553 radiomics features were extracted.After feature dimensionality reduc-tion,XGboost machine learning algorithm was utilized to rank the top fifteen features.Stepwise forward multiple logistic regression was employed to identify the most significant features.The radiomics scores of the intratumor,peritumor 0.3 cm,and intratumor+peritu-mor 0.3 cm were calculated.The diagnostic efficacy of the model was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results The nomogram constructed based on radiomics scores of intratumor,peritumor 0.3 cm,intratumor+peritumor 0.3 cm in the training set for preoperative prediction of poorly differentiated ESCC had an AUC of 0.899[95％confidence interval(CI)0.846-0.938],and it was well validated in the vali-dation set(AUC 0.869,95％CI 0.775-0.934)and the full data set(AUC 0.889,95％CI 0.845-0.924).Additionally,calibration curves and DCA indicated that the nomogram achieved good calibration ability in the three cohorts and offered greater clinical net benefit.Conclusion The nomogram based on enhanced CT venous phase intratumor and peritumor radiomics achieves a high and stable diagnostic efficacy for preoperatively predicting poorly differentiated ESCC,which may help with individualized surgical selec-tion and management before surgery.

Objective:To analyze the association between moderate-to-vigorous-physical activity (MVPA) and obesity, poor sleep quality, as well as multimorbidity in 7- to 8-year-old children in Shanghai City.Methods:From September to November 2023, a cluster sampling method was used to select second-grade students from four primary schools in Jinshan District, Shanghai. Three-axis acceleration motion sensors (GT3X+, Acti-graph) were used to monitor daily physical activity for seven consecutive days. A multivariate logistic regression model was used to analyze the association between MVPA duration characteristics and obesity, poor sleep quality and multimorbidity in school-age children.Results:Of the 937 study participants, 512 (54.64%) were boys and 425 (45.36%) were girls. Among them, 89 (9.50%) were obese and 782 (83.46%) had poor sleep quality. A total of 77 cases (8.22%) were affected by obesity and poor sleep quality. The average daily MVPA time was (45.97±15.87) minutes, and the MVPA attainment rate was 17.18%. The multivariate logistic regression model analysis showed that, after adjusting for covariates, the daily average MVPA time was negatively associated with the risk of obesity ( OR=0.982, 95% CI: 0.968-0.997), as well as multimorbidity ( OR=0.981, 95% CI: 0.965-0.997). The risk of obesity, poor sleep quality and multimorbidity in <1 d was 2.228 ( OR=2.228, 95% CI: 1.398-3.549), 1.702 ( OR=1.702, 95% CI: 1.141-2.540) and 2.150 ( OR=2.150, 95% CI: 1.310-3.528) times higher than that in ≥1 d. Conclusion:Obesity, poor sleep quality and multimorbidity of school-age children are closely related to the level of moderate-to-vigorous physical activity.

Objective:To investigate the protocol and clinical efficacy of hair follicle-bearing microskin (HF-MS) transplantation in the treatment of hypertrophic scars.Methods:Prospective randomized controlled trial. From January to November 2024, patients with hypertrophic scars were recruited from the Medical Cosmetic Center of Affiliated Hangzhou First People’s Hospital with Westlake University School of Medicine and the Department of Plastic and Reconstructive Surgery of Ningbo Sixth Hospital. Patients were randomly divided into the observation group and the control group using a random number table. In the observation group, 1.0 mm punch decompression was performed on the hypertrophic scar area, followed by implantation of HF-MS extracted from the scalp donor site using follicular unit excision (FUE) into the decompression pores. The control group underwent only 1.0 mm punch decompression. Vancouver scar scale (VSS) scores (total score 0-15, higher scores indicating more severe scarring) were assessed preoperatively and at 1, 3, and 6 months postoperatively. Efficacy at 6 months, improvement in hypertrophic scar area, hair survival rate (observation group), adverse reactions, and patients’ satisfaction rates were evaluated. Categorical data were expressed as frequency (%) and analyzed using chi-square tests; normally distributed measurement data were expressed as Mean ± SD and analyzed using independent samples t-tests. Results:A total of 50 patients were included (25 per group), with 22 males and 28 females, aged 18-60 years (mean age: 33 years). The effective rate was 92% (23/25) in the observation group and 68% (17/25) in the control group, showing a statistically significant difference ( P<0.05). Preoperative VSS scores did not differ significantly between the observation and control groups [(6.67±3.19) vs. (7.12±2.89), P>0.05]. At 1, 3, and 6 months postoperatively, the observation group had VSS scores of (5.48±2.60), (4.64±2.39), and (3.80±2.10), respectively, compared to (6.36±2.53), (5.84±2.28), and (5.32±2.09) in the control group. The 6-month postoperative VSS scores differed significantly between groups ( P<0.05). Preoperative hypertrophic scar areas showed no significant difference [(5.75±2.83) cm 2 vs. (6.91±3.31) cm 2,P>0.05]. At 6 months postoperatively, the observation group had significantly smaller scar areas than the control group [(3.15±1.55) cm 2 vs. (5.37±2.93) cm 2,P<0.01]. The average hair survival rate in the observation group was 41% at 6 months. Adverse reactions occurred in 3 cases in the observation group (2 skin indurations, 1 hyperpigmentation) and 7 cases in the control group (4 hyperpigmentation, 2 skin atrophy, 1 skin induration). The observation group had a significantly lower adverse reaction rate [12% (3/25) vs. 28% (7/25), P<0.05]. Patient satisfaction rates were 88% (22/25) in the observation group and 64% (16/25) in the control group ( P<0.05). Conclusion:HF-MS transplantation demonstrates definitive clinical efficacy in treating hypertrophic scars, effectively improving scar morphology, clinical symptoms, and patient quality of life.

The National Essential Medicines System could protect public health and ensure access to essential medications.Although the current selection methods for China's National Essential Medicines Lists(NEMLs)are becoming more scientific and standardized,there are still problems such as much emphasis on expert experience and the lack of transparency of decision-making basis.To address these issues,it proposes an evidence-based decision-making pathway for NEMLs selection guided by clinical value.This approach ensures a strong integration of evidence and decision-making,offering valuable insights for improving the adjustment procedures and selection criteria of the NEMLs in China.

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.