The morphological changes and functions of mitochondria are closely associated with the development and progression of non-alcoholic fatty liver disease （NAFLD）. Dynamin-related protein 1 （Drp1） is one of the primary proteins determining mitochondrial fission， and its activity is strictly controlled to ensure the balance of mitochondrial dynamics according to cellular needs. Drp1 can enhance mitochondrial interactions and mitochondrial fission by promoting the formation of endoplasmic reticulum tubules， and the phosphorylation state and deacetylation of Drp1 can also affect the morphological changes of mitochondria， thereby affecting the status of NAFLD. This article elaborates on the role and mechanism of action of Drp1 in the progression of NAFLD， in order to provide ideas for targeted therapy for NAFLD.

Objective: To analyze the trend in burden of rheumatoid arthritis (RA) in China from 1990 to 2021, so as to provide insights into reducing the RA burden in China. Methods: Data of Global Burden of Disease Study 2021 were collected, and the incidence, mortality and disability-adjusted life years (DALY) of RA in China from 1990 to 2021 were analyzed and compared with global and different Socio-demographic Index (SDI) regions. The trend in burden of RA was analyzed using average annual percent change (AAPC). Results: The crude incidence rates of RA in China increased from 10.87/105 in 1990 to 17.38/105 in 2021, the crude mortality rates increased from 0.41/105 to 0.72/105, and the crude DALY rates increased from 34.26/105 to 58.61/105, with the increases of 59.98%, 77.95% and 71.06%, respectively. From 1990 to 2021, the standardized incidence rates of RA in China showed an increasing trend (AAPC=0.545%, P<0.05), the standardized mortality rates showed a decreasing trend (AAPC=-0.783%, P<0.05), and the standardized DALY rates showed no significant trend (AAPC=-0.017%, P>0.05). In 2021, the standardized incidence rate, standardized mortality rate and standardized DALY rate of RA were higher in females than in males; from 1990 to 2021, the standardized DALY rates of RA showed a decreasing trend in females (AAPC=-0.200%, P<0.05) and an increasing trend in males (AAPC=0.316%, P<0.05). The crude incidence rates of RA first increased and then decreased with age in 2021, reaching the highest in the age group of 75-<80 years at 34.36/105. Both the crude mortality rates and the crude DALY rates increased with age, reaching the highest in the age group of 95 years and older at 26.72/105 and 285.67/105, respectively. The standardized incidence rates and standardized DALY rates of RA in China in 2021 were lower than those in high SDI regions, while the standardized mortality rate was lower than that in medium-low SDI regions. Conclusions The burden of RA in China from 1990 to 2021 showed an upward trend, and was at a high level compared to different SDI regions. Higher disease burden of RA was seen in females and the elderly.

Objective: To construct a prediction model for sleep disorders in shift workers of a chemical fiber enterprise, so as to provide the basis for early identification and prevention of sleep disorders in shift workers. Methods: Shift workers were sampled from a chemical fiber enterprise in Tongxiang City, Zhejiang Province using a cluster sampling method from August 2022 to July 2024. Demographic information, length of service and average weekly working hours were collected through questionnaire surveys. Depressive symptoms, anxiety symptoms and sleep disorders were evaluated using the Pittsburgh Sleep Quality Index, Patient Health Questionnaire and Generalized Anxiety Disorder Questionnaire, respectively. The shift workers were randomly divided into a training set and a validation set at a ratio of 7∶3. Predictive factors were selected using a multivariable logistic regression model based on the training set, and a nomograph model for prediction of sleep disorders in shift workers was established. The predictive values of the model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve based on the training set and validation set. Results: Totally 673 shift workers were included, with a median age of 32 (interquartile range, 12) years. There were 493 males, accounting for 73.25%. There were 471 (69.99%) workers in the training set and 202 (30.01%) workers in the validation set. There were 274 workers with sleep disorders, accounting for 40.71%. The equation for the prediction model was ln[p/(1-p)]=-8.391+1.906×average weekly working hours+1.822×depressive symptoms+1.667×anxiety symptoms. The area under the ROC curve was 0.769 (95%CI: 0.661-0.835) for the training set and 0.655 (95%CI: 0.593-0.737) for the validation set, and Hosmer-Lemeshow test showed a good fitting effect (both P>0.05). Conclusion The nomograph model constructed by average weekly working hours, depressive symptoms and anxiety symptoms can be used to predict the risk of sleep disorders in shift workers of a chemical fiber enterprise.

As people’s attention to health continues to increase, the market demand for traditional Chinese medicine (TCM) is growing steadily. The quality and safety of Chinese medicinal materials have attracted unprecedented social attention. In particular, the issue of exogenous harmful residue pollution in TCM has become a hot topic of concern for both regulatory authorities and society. The Chinese Pharmacopoeia 2025 Edition further refines the detection methods and limit standards for exogenous harmful residues in TCM. This not only reflects China’s high-level emphasis on the quality and safety of TCM but also demonstrates the continuous progress made by China in the field of TCM safety supervision. Basis on this study, by systematically reviewing the development history of the detection standards for exogenous harmful residues in TCM and analyzing the revisions and updates of these detection standards in the Chinese Pharmacopoeia 2025 Edition, deeply explores the key points of the changes in the monitoring standards for exogenous harmful residues in TCM in the Chinese Pharmacopoeia 2025 Edition. Moreover, it interprets the future development directions of the detection of exogenous residues in TCM, aiming to provide a reference for the formulation of TCM safety supervision policies.

ObjectiveThis study aims to investigate the dynamic changes in general body parameters, blood glucose, and blood lipid profiles in obese cynomolgus monkeys, exploring the correlations among these parameters and providing a reference for research on the obese cynomolgus monkey model. Methods30 normal male cynomolgus monkeys aged 5 - 17 years old (with body mass index < 35 kg/m² and glycated hemoglobin content < 4.50%) and 99 spontaneously obese male cynomolgus monkeys (with body mass index ≥35 kg/m² and glycated hemoglobin content < 4.50%) were selected. Over a period of three years, their abdominal circumference, skinfold thickness, body weight, body mass index, fasting blood glucose, glycated hemoglobin, and four blood lipid indicators were monitored. The correlations between each indicator were analyzed using repeated measurement ANOVA, simple linear regression, and multiple linear regression correlation analysis method. Results Compared to the control group, the obese group exhibited significantly higher levels of abdominal circumference, skinfold thickness, body weight, body mass index, and triglyceride (P＜0.05). In the control group, skinfold thickness increased annually, while other indicators remained stable. Compared with the first year, the obese group showed significantly increased abdominal circumference, skinfold thickness, body weight, body mass index, triglyceride, and fasting blood glucose in the second year(P＜0.05), with this increasing trend persisting in the third year (P＜0.05). In the control group, the obesity incidence rates in the second and third years were 16.67% and 23.33%, respectively, while the prevalence of diabetes remained at 16.67%. In the obese group, the diabetes incidence rates were 29.29% and 44.44% in years 2 and 3, respectively. Among the 11-13 year age group, the incidence rates were 36.36% and 44.68%, while for the group older than 13 years, the rates were 28.13% and 51.35%. Correlation analysis revealed significant associations (P＜0.05) between fasting blood glucose and age, abdominal circumference, skinfold thickness, body weight, and triglyceride in the diabetic monkeys. Conclusion Long-term obesity can lead to the increases in general physical indicators and fasting blood glucose levels in cynomolgus monkeys, and an increase in the incidence of diabetes. In diabetic cynomolgus monkeys caused by obesity, there is a high correlation between their fasting blood glucose and age, weight, abdominal circumference, skinfold thickness, and triglyceride levels, which is of some significance for predicting the occurrence of spontaneous diabetes.

Objective To develop and validate a model to predict the risk of radiation-induced lung injury (RILI) and assess its clinical feasibility. Methods Clinical data from 125 patients with non-small cell lung cancer (NSCLC) were included in the study. The patients were divided into training group (88 cases) and validation group (38 cases). Key predictive factors were identified using univariate and multivariate logistic regression analyses combined with least absolute shrinkage and selection operator (LASSO) regression. A predictive model was constructed and evaluated using a nomogram, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Results The key variables identified by the model were tumor volume (P = 0.017), Eastern Cooperative Oncology Group performance status score (P = 0.035), 95% of the minimum dose to the target volume (P = 0.028), percentage of bilateral lung volume receiving 20 Gy of radiation (P < 0.001), and neutrophil-to-lymphocyte ratio (P = 0.021). The ROC curve showed that the areas under the curve (AUC) for the model in the training and validation groups were 0.987 and 0.992, respectively, indicating good predictive ability. The calibration curve and decision curve further confirmed the accuracy and clinical practicability of the model. Conclusion The predictive model proposed in this study can accurately assess the risk of developing RILI in patients with NSCLC who have undergone radiotherapy, demonstrating its potential value in clinical practice.

In recent years, with the improvement of people's awareness of physical examination and the more accurate detection equipment, the detection rate of pulmonary nodules is getting higher and higher. Surgical resection is the first choice for the treatment of malignant pulmonary nodules, but multiple pulmonary nodules, nodules in complex areas and those with surgical contraindications are not suitable for surgery. As an effective, less invasive and low-cost treatment, ablation has developed rapidly in the treatment of multiple pulmonary nodules. This article introduces the progress of several common ablation techniques (radiofrequency ablation, microwave ablation, cryoablation) in the treatment of multiple pulmonary nodules, the indications and contraindications of ablation techniques, the efficacy evaluation and complications after ablation therapy, and the prospects of ablation techniques in the treatment of multiple pulmonary nodules.

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.

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.

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.