ObjectiveTo investigate the independent risk factors for decompensation in patients with liver cirrhosis， to construct a nomogram-based risk assessment model， and to assess its risk assessment performance and clinical value by comparing it with Model for End-Stage Liver Disease （MELD）， MELD combined with serum sodium concentration （MELD-Na）， and MELD 3.0 scoring system. MethodsA retrospective analysis was performed for 514 patients with liver cirrhosis who attended The First Hospital of Shanxi Medical University from January 2020 to May 2025， and related data were collected， including demographic data and laboratory markers. According to the presence or absence of decompensation， the patients were divided into compensation group with 275 patients and decompensation group with 239 patients. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator regression analysis was used for screening of variables， and the multivariate logistic regression analysis was used to identify independent risk factors for decompensated liver cirrhosis and construct a nomogram model. The receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis were used to assess the discriminatory ability， calibration， and net clinical benefit of the model. ResultsThe multivariate logistic regression analysis showed that low hemoglobin （odds ratio ［OR］=0.984， 95% confidence interval ［CI］： 0.969　—　0.999， P<0.05）， low lymphocytes （OR=0.564， 95%CI： 0.383　—　0.830， P<0.05）， high international normalized ratio （OR=3.131， 95%CI： 1.242　—　7.891， P<0.05）， and low serum sodium （OR=0.922， 95%CI： 0.872　—　0.975， P<0.05） were independent risk factors for decompensation events in patients with liver cirrhosis. Construct the prediction model equation： Logit（P）=intercept value－0.016×hemoglobin－0.573×lymphocytes+1.141×INR－0.081×serum sodium. The nomogram constructed based on these factors had a good discriminatory ability， with an area under the ROC curve （AUC） of 0.787， a specificity of 77.8%， and a sensitivity of 67.4%， and it had significantly better predictive performance than MELD score （AUC=0.718）， MELD-Na score （AUC=0.719）， and MELD 3.0 score （AUC=0.725）. The calibration curve showed good consistency between the probability of risk assessed by the model and the observed probability. The decision curve analysis showed that compared with the MELD-based scores， this model provided higher net clinical benefit across a wide range of risk thresholds. ConclusionThis study successfully constructed and validated a nomogram risk assessment model incorporating hemoglobin， international normalized ratio， lymphocytes， and serum sodium. This model has good clinical practicability and can help to achieve early identification of high-risk patients with decompensated liver cirrhosis and optimize intervention strategies in clinical practice.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

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.

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

Quality control is a key link in the modernization process of traditional Chinese medicine(TCM). Studies have shown that the effects of active components in TCM depend on not only their chemical composition but also their suitable physical forms and states. The physical phase structures, such as micelles, vesicles, gels, and nanoparticles, can improve the solubility, delivery efficiency, and targeting precision of active components. These structures significantly enhance the pharmacological activity while reducing the toxicity and side effects, demonstrating functional activity surpassing that of active components and highlighting the key effects of "structures" on "functions" of active components. Taking the physical phase structure as a breakthrough point, this paper outlines the common types of TCM physical phase structures. Furthermore, this paper explores how to realize the quality upgrading of TCM through the precise regulation of physical phase structures based on the current applications and potential of TCM physical phase structures in processing to increase the efficacy and reduce the toxicity, compounding and decocting processes, drug delivery systems, and quality control, aiming to provide novel insights for the future quality control of TCM.
Quality Control ; Drugs, Chinese Herbal/standards* ; Medicine, Chinese Traditional/standards* ; Humans ; Drug Delivery Systems

Objective To understand the individual radiation dose levels and changes of occupational external exposure among radiation workers in a steel group from 2020 to 2023, and to provide a basis for strengthening radiation protection in the group. Methods According to the Specifications for individual monitoring of occupational external exposure (GBZ 128-2019), individual monitoring was conducted using a thermoluminescent dosimeter, with each monitoring cycle lasting 90 days, totaling four cycles per year. Individual monitoring data of occupational external exposure among radiation workers in the steel group from 2020 to 2023 were collected for statistical analysis. Results A total of 5889 monitoring records were collected from radiation workers across 37 affiliated units of the steel group during the period of 2020-2023. The average annual effective dose was 0.150 mSv, the maximum annual effective dose was 3.84 mSv, and 0.3% monitored workers showed annual effective doses exceeding 1 mSv. The average annual effective dose in 2023 was 0.146 mSv, which was lower than in 2020 but higher than in 2021 and 2022 (Z = −6.657 to 14.129, P < 0.001). The number of monitored workers in industrial and medical sectors accounted for 86.9% and 13.1%, respectively. Their average annual effective doses were 0.152 mSv and 0.140 mSv, respectively, and the difference was statistically significant (Z = −7.969, P < 0.001). In the industrial sector, the average annual effective dose for the monitored workers in industrial radiography was significantly higher than other occupations (Z = −8.094, P < 0.001). There was a significant difference in average annual effective dose among workers of different occupational categories in the medical sector (H = 67.040, P < 0.001). The average annual effective doses were relatively high for diagnostic radiology and nuclear medicine. Additionally, the average annual effective doses for male and female radiation workers were 0.150 mSv and 0.152 mSv, respectively (P > 0.05). Conclusion The average annual effective dose of radiation workers in the steel group was at low levels for four consecutive years, which meet the requirements of the national standard. However, to maintain the individual radiation dose levels of radiation workers as low as reasonably achievable, it remains necessary to strengthen radiation protection management in the steel group.

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.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

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 analyze the perceptions, attitudes toward cognitive screening and associated factors in Chinese population.Methods:It was a cross-sectional study, a total of 1 246 Chinese residents who used smartphones and completed the cognitive screening survey in the Sojump application from February 22 to March 7, 2024 were consecutively selected as the study subjects. The questionnaire content included demographic data, physical examination information, perceptions of cognitive disorders, perceptions, attitudes and suggestions of cognitive screening. A total of 1 273 questionnaires were distributed, and 1 273 were retrieved, of which 1 246 were valid (97.9%). The logistic regression analysis was used to evaluate factors associated with the attitudes toward cognitive screening in the subjects.Results:Of the 1 246 respondents included in the study, 468 were male and 778 were female, with a mean age of (43.9±13.8) years. The respondents covered 26 provincial-level administrative regions in China, including 347 (27.8%) in the east, 429 (34.4%) in the middle and 470 (37.7%) in the west. While 943 respondents failed to comprehend the cognitive screening, 914 considered it necessary. Additionally, 447 respondents recommended initiating cognitive screening at age 50, 927 respondents recommended annual screening, and 924 respondents preferred scale assessment. Female ( OR=2.121, 95% CI: 1.599-2.815), middle-aged and elderly ( OR=1.681, 95% CI: 1.223-2.310), urban residents ( OR=1.426, 95% CI: 1.002-2.029), high per capita monthly household income ( OR=1.253, 95% CI: 1.063-1.477), had complete physical examination ( OR=1.404, 95% CI: 1.015-1.943), better understanding of cognitive disorders ( OR=2.202, 95% CI: 1.750-2.772), and better understanding of cognitive screening ( OR=3.313, 95% CI: 2.227-4.931) showed positive correlations with the attitude favoring cognitive screening (all P<0.05). Conclusion:The perception levels of cognitive screening among Chinese residents are relatively low, but their attitudes are positive. Socio-economic factors, behavioral experiences, and knowledge levels are associated with residents′ attitudes toward cognitive screening.