ObjectiveTo investigate the effect of transcutaneous electrical acupoint stimulation (TEAS) on pulmonary function, exercise endurance and clinical symptoms in patients with chronic obstructive pulmonary disease (COPD). MethodsRandomized controlled trials on the effect of TEAS on COPD were systematically searched in CNKI, Wanfang Data, VIP Database, SinoMed, PubMed, Embase and Cochrane Library from inception to March, 2025. Two researchers independently screened the literature and extracted data. The methodological quality of the included researches was assessed using the Cochrane Handbook for Systematic Reviews of Interventions version 5.1.0 risk-of-bias tool and the PEDro scale. Statistical analysis was performed using RevMan 5.4. ResultsA total of eleven studies involving 542 patients were included. Scores of PEDro scale ranged from five to nine. TEAS significantly improved forced expiratory volume in one second (FEV₁) (MD = 0.19, 95%CI 0.08 to 0.29, P < 0.001), FEV₁/forced vital capacity (MD = 5.15, 95%CI 4.21 to 6.09, P < 0.001), FEV₁% predicted (MD = 8.46, 95%CI 5.71 to 11.22, P < 0.001), peak expiratory flow (MD = 0.18, 95%CI 0.05 to 0.31, P = 0.006), and COPD Assessment Test (CAT) score (MD = -3.60, 95%CI -4.81 to -2.40, P < 0.001). Distance of 6-minute walk test increased significantly (MD = 25.97, 95%CI 7.88 to 44.05, P = 0.005), however, after sensitivity analysis, the magnitude of improvement was limited and did not reach the minimal clinically important difference threshold. Subgroup analysis showed that multi-acupoint intervention was more effective than single-acupoint intervention (MD = 0.29 vs. 0.12), and one to two weeks of intervention was the most effective (MD = 0.37). TEAS combined with conventional medication or exercise training showed synergistic benefits. ConclusionTEAS helps improve pulmonary function and clinical symptoms in patients with COPD. TEAS combined with conventional interventions may produce synergistically enhanced effects.

Wilson disease （WD） is a hereditary disorder of copper metabolism characterized by abnormal copper accumulation in tissues， including the liver and brain， which leads to severe hepatic and neurological damage. This disease is often accompanied by lipid metabolism abnormalities， and the exploration of related mechanisms has attracted increasing attention. This article introduces the clinical features of lipid metabolism disorders in WD patients， summarizes the research advances in the serum levels of lipids and hepatic steatosis， analyzes the potential mechanisms of the interaction between copper and lipid metabolism， and highlights the significance of lipid-related molecules in disease diagnosis and clinical evaluation. In clinical practice， the monitoring and assessment of lipid metabolism parameters should be taken seriously in patients with WD， in order to promote comprehensive disease management and improve the prognosis of patients.

ObjectiveTo investigate the effect of Zishen Huoxue Formula （ZSXHF） on molecular-targeted therapy-associated proteinuria in patients with primary liver cancer （PLC）， to assess the efficacy of ZSXHF in the treatment of molecular-targeted therapy-associated proteinuria， and to provide a basis for clinical medication. MethodsA retrospective cohort study was conducted among the PLC patients with molecular-targeted therapy-associated proteinuria who were diagnosed and treated in The Department of Hepatology of Chinese PLA General Hospital， from January 1， 2022 to July 1， 2025. With ZSXHF treatment as the exposure factor， the patients with a cumulative treatment duration of ≥9 weeks were enrolled as traditional Chinese medicine （TCM） group， while those without TCM treatment were enrolled as control group. Propensity score matching was performed for the two groups at a ratio of 1∶1 based on sex， age， 24-hour urinary protein， blood urea nitrogen， and serum creatinine. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between groups. Univariate and multivariate Logistic regression analyses were used to investigate the influencing factors for promoting the improvement of targeted-therapy-associated proteinuria. ResultsA total of 137 PLC patients with targeted-therapy-associated proteinuria were enrolled， with 34 patients in the TCM group and 103 in the control group. After follow-up for 6 months， the TCM group had a significant improvement in urinary protein grade compared with the control group （χ²=9.261， P=0.016）. There were 25 patients in each group after propensity score matching， and after follow-up for 6 months， there were significant differences between the two groups in urinary protein grade （χ²=15.689， P<0.001） and 24-hour urinary protein （Z=-3.075， P=0.002）. After cumulative treatment with ZSXHF for ≥9 weeks， the TCM group had a significantly greater change in 24-hour urinary protein from baseline compared with the control group （t=-2.514， P=0.016）， while there were no significant differences in the changes in liver and renal function after ZSXHF intervention between the two groups （all P>0.05）. The multivariate Logistic regression analysis showed that ZSXHF treatment （odds ratio=2.901， 95% confidence interval： 1.135 — 7.417， P=0.026） was an independent influencing factor for improvement in molecular-targeted therapy-associated proteinuria. ConclusionZSHXF can effectively alleviate molecular-targeted therapy-associated proteinuria in PLC patients with a favorable safety profile， which provides a new reference for TCM prevention and treatment of molecular-targeted therapy-associated adverse reactions in PLC patients.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

SAMHD1 protein was first identified as a crucial intrinsic antiviral factor in 2011 and is also associated with autoimmune diseases such as Aicardi-Goutières syndrome(AGS) and familial chilblain lupus.In recent years, it has become a focal point of research in the field of molecular biology.SAMHD1 is primarily expressed in cells such as dendritic cells and macrophages, which prevents retroviral replication by decreasing intracellular deoxyribonucleoside triphosphate(dNTP)levels.The Vpx protein of human immunodeficiency virus(HIV)-2/simian immunodeficiency virus(SIV) antagonizes SAMHD1 function.Recent studies have also indicated that the activity of SAMHD1 is influenced by multiple factors and can regulate LINE-1(long interspersed element 1, L1) activity in tumor cells.This paper comprehensively analyzes the complex antiviral mechanisms of the SAMHD1 protein, as well as its mechanism against LINE-1, and also covers the latest understanding of its biological functions and structural aspects.

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.