To introduce the general thinking, guidelines, work objectives and elaboration process of the general technical requirements adopted by volume Ⅳ of the Chinese Pharmacopoeia 2025 Edition, and to summarize and figure out the main characteristics on dosage forms, physico-chemical testing, microbial and biological testing, reference standards and guidelines The newly revised general chapters of pharmacopoeia give full play to the normative and guiding role of the Chinese Pharmacopoeia standard, track the frontier dynamics of international drug regulatory science and the elaboration of monographs, expand the application of state-of-the-art technologies, and steadily promote the harmonization and unification with the ICH guidelines； further enhance the overall capacity of TCM quality control, actively implement the 3 R principles on animal experiments, and practice the concept of environmental-friendly； replace and/or reduce the use of toxic and hazardous reagents, strengthen the requirements of drug safety control This paper aims to provide a full-view perspective for the comprehensive, correct understanding and accurate implementation of general technical requirements included in the Chinese Pharmacopoeia 2025 Edition.

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 association between mental health and muscle strength among Chinese adolescents aged 13- 18, providing a theoretical foundation and intervention strategies for mental health promotion. Methods: Data were obtained from the 2019 Chinese National Survey on Students Constitution and Health, including 98 631 Chinese adolescents aged 13- 18. Psychological distress was assessed by using the Kessler Psychological Distress Scale (K10), and mental well being was measured with the Warwick-Edinburgh Mental Well being Scale (WEMWBS). Based on the gender and age specific Z scores of various test items [grip strength, standing long jump, pull ups (for males), and sit ups (for females)], muscle strength index (MSI) was constructed to evaluate the comprehensive level of muscle strength in adolescents. According to the Dual factor Model (DFM) of mental health, participants were categorized into four groups:troubled, symptomatic but content, vulnerable, and complete mental health. Gender differences were analyzed by using Chi-square tests, trends were tested with Cochran-Armitage tests, and multinomial Logistic regression models were applied to assess associations between muscle strength and mental health among adolescents. Results: In 2019, 37.4% of Chinese adolescents aged 13-18 were reported of high mental distress, and 59.9% were reported of low mental well being. Boys had significantly lower rates of high mental distress (35.3%) and low mental well being (55.6%) compared to girls (39.4%, 64.3%), and the differences were of statistical significance ( χ 2=176.13, 780.42, both P <0.05). In 2019, the rate of complete mental health among adolescents showed a downward trend with increasing age ( χ 2 trend = 258.47) and a gradual upward trend with increasing muscle strength levels ( χ 2 trend =123.14)，and both boys and girls exhibited similar trends ( χ 2 trend =103.83, 168.46; 57.00 , 67.34) (all P <0.05). The results of the unordered multiclass Logistic regression model showed that after controlling for confounding factors such as age and gender, when the completely pathological group as a reference, for every 1 unit increase in MSI in adolescents, the likelihood of being in a completely mental health state increased by 29% ( OR = 1.29); for every unit increase in the Z-score for pull ups, the likelihood of being in a completely mental health state increased by 6% ( OR =1.06) among boys; for every 1 unit increase in sit up Z score, the likelihood of being in a completely mental health state increased by 19% ( OR =1.19) among girls (all P <0.05). Conclusions The mental health status of Chinese adolescents is not good enough. Muscle strength is positively associated with mental health.

Objective To investigate the association between lifestyle and risk of heart disease and diabetes in the elderly population in Xi'an City. Methods From January 2021 to January 2024, a staged cluster sampling method was used to investigate the lifestyle and the occurrence of heart disease and diabetes in elderly population aged 60 years and above in the communities of Xi'an. Multivariate logistic regression was used to analyze the relationship between lifestyle and the risk of heart disease and diabetes. Results A total of 413 elderly people were investigated, of which 31.96% had heart disease, 27.12% had diabetes, and 10.90% had diabetes with heart disease. Multivariate logistic regression analysis revealed that age, BMI, family history, sweet food preference, smoking, and sitting and lying for a long time were risk factors for diabetes in the elderly population (P<0.05). Age, BMI, family history, history of diabetes, preference for salted products, smoking, drinking, and sitting and lying for a long time were risk factors for heart disease in the elderly population (P<0.05). Conclusion The incidence rates of heart disease and diabetes are high in the elderly population in Xi'an City. The risk of diabetes is related to unhealthy lifestyles such as sweet food preference, smoking, and sitting and lying for a long time, while heart disease is related to unhealthy lifestyles such as preference for salted products, smoking, drinking, and sitting and lying for a long time.

Objective To investigate the association between lifestyle and risk of heart disease and diabetes in the elderly population in Xi'an City. Methods From January 2021 to January 2024, a staged cluster sampling method was used to investigate the lifestyle and the occurrence of heart disease and diabetes in elderly population aged 60 years and above in the communities of Xi'an. Multivariate logistic regression was used to analyze the relationship between lifestyle and the risk of heart disease and diabetes. Results A total of 413 elderly people were investigated, of which 31.96% had heart disease, 27.12% had diabetes, and 10.90% had diabetes with heart disease. Multivariate logistic regression analysis revealed that age, BMI, family history, sweet food preference, smoking, and sitting and lying for a long time were risk factors for diabetes in the elderly population (P<0.05). Age, BMI, family history, history of diabetes, preference for salted products, smoking, drinking, and sitting and lying for a long time were risk factors for heart disease in the elderly population (P<0.05). Conclusion The incidence rates of heart disease and diabetes are high in the elderly population in Xi'an City. The risk of diabetes is related to unhealthy lifestyles such as sweet food preference, smoking, and sitting and lying for a long time, while heart disease is related to unhealthy lifestyles such as preference for salted products, smoking, drinking, and sitting and lying for a long time.

Background The benchmark dose (BMD) method calculates the dose associated with a specific change in response based on a specific dose-response relationship. Compared with the traditional no observed adverse effect level (NOAEL) method, the BMD method has many advantages, and the 95% lower confidence limit of benchmark dose lower limit (BMDL) is recommended to replace NOAEL in deriving biological exposure limits. No authority has yet published any health-based guideline for rare earth elements. Objective To evaluate genotoxicity threshold induced by acute exposure to neodymium nitrate in mice using BMD modeling through micronucleus test and comet assay. Methods SPF grade mice (n=90) were randomly divided into nine groups, including seven neodymium nitrate exposure groups, one control group (distilled water), and one positive control group (200 mg·kg⁻¹ ethyl methanesulfonate), 10 mice in each group, half male and half female. The seven dose groups were fed by gavage with different concentrations of neodymium nitrate solution (male: 14, 27, 39, 55, 77, 109, and 219 mg·kg⁻¹; female: 24, 49, 69, 97, 138, 195, and 389 mg·kg⁻¹) twice at an interval of 21 h. Three hours after the last exposure, the animals were neutralized by cervical dislocation. The bone marrow of mice femur was taken to calculate the micronucleus rate of bone marrow cells, and the liver and stomach were taken for comet test. Results The best fitting models for the increase of polychromatophil micronucleus rate in bone marrow of female and male mice induced by neodymium nitrate were the exponential 4 model and the hill model, respectively. The BMD and the BMDL of female mice were calculated to be 31.37 mg·kg⁻¹ and 21.90 mg·kg⁻¹, and those of male mice were calculated to be 58.62 mg·kg⁻¹ and 54.31 mg·kg⁻¹, respectively. The best fitting models for DNA damage induced by neodymium nitrate in female and male mouse hepatocytes were the exponential 5 model and the exponential 4 model, respectively, and the calculated BMD and BMDL were 27.15 mg·kg⁻¹ and 11.99 mg·kg⁻¹ for female mice, and 16.28 mg·kg⁻¹ and 10.47 mg·kg⁻¹ for male mice, respectively. The hill model was the best fitting model for DNA damage of gastric adenocytes in both female and male mice, and the calculated BMD and BMDL were 36.73 mg·kg⁻¹ and 19.92 mg·kg⁻¹ for female mice, and 24.74 mg·kg⁻¹ and 14.08 mg·kg⁻¹ for male mice, respectively. Conclusion Taken the micronucleus rate of bone marrow cells, DNA damage of liver cells and gastric gland cells as the end points of genotoxicity, the BMDL of neodymium nitrate is 10.47 mg·kg⁻¹, which can be used as the threshold of genotoxic effects induced by acute exposure to neodymium nitrate in mice.