Hepatitis B virus （HBV） is a unique hepatotropic DNA virus that forms covalently closed circular DNA within the nucleus of hepatocytes and can partially integrate into the host genome， establishing the molecular basis for persistent viral infection. HBV infection and replication depends on multiple hepatocyte-enriched host factors and is modulated by the hepatic microenvironment. The host achieves natural control and clearance of HBV through various mechanisms， including cytolytic elimination mediated by cellular immunity such as cytotoxic T lymphocytes and natural killer cells， innate immunity and noncytolytic clearance driven by interferons and various cytokines， and antibody-mediated protection and clearance as part of humoral immune response. In addition， intracellular restriction factors and pathways， hepatocyte turnover through division and replacement， and changes in the hepatic microenvironment （such as the increase in matrix stiffness） collectively influence the efficiency and outcome of viral control and clearance. This article clarifies and elaborates on related mechanisms， so as to deepen the understanding of HBV chronicity， spontaneous resolution， and cure and provide a theoretical basis for optimizing clinical management and developing novel therapeutic strategies.

BACKGROUND:Bone remodeling is the biological basis of orthodontic tooth movement.Type 2 diabetes mellitus leads to metabolic changes in the jaw and alveolar bone,so it is hypothesized that tooth mobility characteristics may be altered in a high-sugar environment.OBJECTIVE:To explore the impact of type 2 diabetes mellitus on orthodontic tooth movement in rats within one tooth movement cycle.METHODS:Seventy-two Sprague-Dawley rats were selected.Forty rats were randomly chosen and fed with a high-fat diet to construct a type 2 diabetes mellitus model.Thirty-two rats that were successfully modeled were randomly divided into a type 2 diabetes mellitus group(n=16)and a diabetic orthodontic group(n=16).The remaining 32 rats were randomly divided into a control group(n=16)and an orthodontic group(n=16).The rats in the orthodontic group and the diabetic orthodontic group were equipped with nickel-titanium coil spring orthodontic force application devices to move the unilateral maxillary first molars mesially with a force of 50 g.The rats were anesthetized and sacrificed on the 3rd,7th,14th,and 21st days after orthodontic treatment,and Micro-CT was used to measure the mesial displacement of the first molars and detect the changes in the bone microstructure parameters on the tension side.RESULTS AND CONCLUSION:There were significant differences in the tooth movement distances among the four groups of rats on the 3rd,7th,14th,and 21st days of orthodontic treatment(P＜0.05).There were significant differences in bone mineral density,bone volume fraction and trabecular bone separation on the tension side among the four groups on the 7th,14th,and 21st days of orthodontic treatment(P＜0.05).There were differences in the trabecular thickness among the four groups on the 3rd and 14th days of orthodontic treatment(P＜0.05).The diabetic orthodontic group had the smallest tension-side alveolar bone mineral density,bone volume fraction,and trabecular thickness,and the largest tooth movement distance and trabecular separation on the 21st day of orthodontic treatment.The above results indicate that type 2 diabetes mellitus adversely affects bone microstructural parameters on the tension side in orthodontic tooth movement in rats,suggesting the occurrence of an osteoporotic state.

BACKGROUND:Bone remodeling is the biological basis of orthodontic tooth movement.Type 2 diabetes mellitus leads to metabolic changes in the jaw and alveolar bone,so it is hypothesized that tooth mobility characteristics may be altered in a high-sugar environment.OBJECTIVE:To explore the impact of type 2 diabetes mellitus on orthodontic tooth movement in rats within one tooth movement cycle.METHODS:Seventy-two Sprague-Dawley rats were selected.Forty rats were randomly chosen and fed with a high-fat diet to construct a type 2 diabetes mellitus model.Thirty-two rats that were successfully modeled were randomly divided into a type 2 diabetes mellitus group(n=16)and a diabetic orthodontic group(n=16).The remaining 32 rats were randomly divided into a control group(n=16)and an orthodontic group(n=16).The rats in the orthodontic group and the diabetic orthodontic group were equipped with nickel-titanium coil spring orthodontic force application devices to move the unilateral maxillary first molars mesially with a force of 50 g.The rats were anesthetized and sacrificed on the 3rd,7th,14th,and 21st days after orthodontic treatment,and Micro-CT was used to measure the mesial displacement of the first molars and detect the changes in the bone microstructure parameters on the tension side.RESULTS AND CONCLUSION:There were significant differences in the tooth movement distances among the four groups of rats on the 3rd,7th,14th,and 21st days of orthodontic treatment(P＜0.05).There were significant differences in bone mineral density,bone volume fraction and trabecular bone separation on the tension side among the four groups on the 7th,14th,and 21st days of orthodontic treatment(P＜0.05).There were differences in the trabecular thickness among the four groups on the 3rd and 14th days of orthodontic treatment(P＜0.05).The diabetic orthodontic group had the smallest tension-side alveolar bone mineral density,bone volume fraction,and trabecular thickness,and the largest tooth movement distance and trabecular separation on the 21st day of orthodontic treatment.The above results indicate that type 2 diabetes mellitus adversely affects bone microstructural parameters on the tension side in orthodontic tooth movement in rats,suggesting the occurrence of an osteoporotic state.

Objective To investigate serum HCV-RNA detection rate in patients with positive hepatitis C virus (HCV) immunoglobulin G (IgG) antibody, and to analyze the related factors of HCV-RNA detection in patients with positive HCV IgG antibody by logistic regression analysis. Methods A retrospective study was conducted on 500 patients with positive HCV IgG antibody. The positive rate of HCV-RNA was counted. Logistic regression analysis was adopted to analyze the related factors of HCV-RNA detection in HCV IgG antibody positive population. Results A total of 317 patients with positive HCV-RNA detection were included in serum HCV-RNA positive group (n=317), and 183 patients with negative HCV-RNA detection were enrolled as serum HCV-RNA negative group (n=183). The diagnosis rate of HCV infection was 63.40% (317/500). Logistic regression analysis showed that abnormal levels of serum ALT, AST, GGT, TBIL, HCV IgG antibody, prothrombin time, and the presence of clinical symptoms were independently correlated with the detection of HCV-RNA in HCV IgG antibody positive individuals (P<0.05). Established 7-factor prediction models and created a nomogram. The chi-square value was 0.586, and P = 0.89 > 0.05. The ROC curve was plotted using pROC, and the area under the ROC curve was 0.912. Conclusion Among the population positive for HCV IgG antibody, the detection rate of serum HCV-RNA is high. Abnormal levels of ALT, AST, GGT, TBIL, IgG antibody, prothrombin time and clinical symptoms are the related factors for the detection of HCV-RNA. These indicators provide a reference for the clinical judgment of the real infection risk of HCV IgG positive individuals.

ObjectiveTo analyze the composition of respiratory virus pathogen spectrum and epidemiological characteristics among adult influenza-like illness (ILI) cases in Putuo District of Shanghai from 2015 to 2024, to summarize the epidemic patterns of respiratory viruses across different time periods and populations, and to provide scientific evidence for the prevention and control of adult respiratory virus infections in Putuo District of Shanghai. MethodsSurveillance data of 2 273 adult ILI cases from sentinel hospitals under the comprehensive surveillance system for acute respiratory infections in Putuo District of Shanghai from 2015 to 2024 were retrospectively collected. Descriptive epidemiological methods were adopted to analyze the temporal and population distribution characteristics of respiratory viruses, including influenza virus (IFV), respiratory syncytial virus (RSV), adenovirus (ADV), and human metapneumovirus (HMPV). The mixed infection status among different age groups was compared, and the distribution of influenza virus subtypes was described. SPSS 22.0 and WPS Office software were used for statistical analyses. ResultsThe overall detection rate of respiratory viruses in 2 273 adult ILI cases was 43.51%. The detection rate in females (45.48%) was significantly higher than that in males (41.25%), with statistical significance (χ²=4.113, P=0.043). Individuals aged 60 years old and above had the highest detection rate (47.41%) (χ²=8.430, P=0.014). The detection rates in winter (50.41%) and summer (48.42%) were higher than those in other seasons (χ²=47.028, P<0.001). The overall viral detection rate decreased after 2020 (χ²=208.411, P<0.001). Detection rates of various viruses varied by year, with IFV and ADV showing high rates from 2015 to 2019 (all P<0.001), while human coronavirus (HCoV) and rhinovirus/enterovirus (HRV/EV) exhibited higher rates from 2020 to 2022 (all P<0.001). Co-infection with IFV and HCoV was the most common, followed by co-infection with IFV and ADV, together accounting for 50.00% of all mixed infections. During the surveillance period, the detection rate of IFV was significantly higher than that of other respiratory viruses. Influenza B/Victoria lineage predominated in 2021, followed by successive predominance of influenza A(H1N1), A(H3N2), and the co-circulation of the two subtypes from 2022 to 2023. Influenza A(H1N1) re-emerged as the predominant subtype in the first half of 2024. ConclusionFrom 2015 to 2024, respiratory virus infections in adults prevailed in winter and summer, and people aged 60 years old and above were the key high-risk population. The detection rates of respiratory viruses declined after 2020. IFV was the predominant pathogen detected, and different influenza virus subtypes circulated alternately each year.

During the career life cycle, workers may face various health problems such as occupational injuries, occupational diseases, and work-related diseases. How to comprehensively protect the health of workers is a crucial scientific issue that needs to be solved urgently. Workers show the characteristics of co-exposure to multiple occupational risks or co-existence of multiple health conditions in their occupational activities. Occupational injuries are closely related to occupational diseases and work-related diseases. To carry out prevention and control of occupational injuries in the context of "big health", we should further strengthen the systematic approach and highlight the concept of "overall process" and "all-round". That is to establish an occupational injury surveillance system covering the whole process of surveillance-assessment-intervention and the evaluation of intervention effects, and to set up the joint prevention and control strategy of occupational injuries, occupational diseases, and work-related diseases. This will promote the implementation of efficient and intensive health management at government, society, employers, workers and other levels to achieve all-round protection of workers' health. When exploring the possible effects of job burnout, occupational stress, comorbidity, and other factors on occupational injuries, the introduction of machine learning methods provides a new approach to identifying and analyzing the influencing factors of occupational injuries and to exploring potential underlying mechanisms.

Background Workers in the cable manufacturing industry are exposed to high-speed machinery and equipment for a long time, coupled with heavy workload, which poses significant risks to their physical health. However, the issue of occupational injuries in this industry has not received enough attention yet. Objective To understand the incidence of occupational injury of workers in cable manufacturing industry and to analyze the influencing factors. Method A basic information questionnaire and an occupational injury questionnaire were developed to investigate the occupational injuries of 1 343 workers in a cable manufacturing enterprise in the past year, and a total of 1 225 valid questionnaires were recovered, with an effective rate of 91.2%. Descriptive statistics were used to characterize the causes, injury locations, injury types, and other characteristics of employees’ occupational injuries. Chi-square test was used to analyze the occupational injury status of groups with different demographic characteristics, occupational characteristics, lifestyles, and interpersonal relationships. Logistic regression was used to analyze the influencing factors of occupational injuries. Result The incidence of occupational injuries among workers in a cable manufacturing enterprise in the past year was 8.6%, which mainly happened in male workers (80.0%) and occurred from May to July in summer (45.7%). The main causes were mechanical injuries (32.4%) and object blows (27.6%). The main sources of damage were machinery and equipment (36.2%) as well as raw materials and products (15.2%). The main injuries were located in upper limbs (53.3%) and lower limbs (22.9%). The main types of injuries were fractures (33.3%) and abrasions/contusions/puncture wounds (19.0%). The results of univariate analysis showed that there were statistically significant variations in the incidence of occupational injuries by gender, overtime, pre-job training, years of service in current position, alcohol consumption, physical exercise per week, and co-worker relationship (P<0.05). The logistic regression model showed that workers who exercised less than twice a week, did not participate in pre-job training, worked overtime, and had fair/poor/very poor colleague relationship had a higher risk of occupational injury, while women had a lower risk of occupational injury. Conclusion The distribution of occupational injury population is mainly male, and the time distribution is mainly from May to July. Gender, physical exercise, pre-job training, overtime, and colleague relationship are the influencing factors of occupational injuries. We should strengthen pre-job training, arrange work hours reasonably, and create a good working atmosphere to reduce the occurrence of occupational injuries.

Background Social psychological factors have emerged as a key area of research in occupational injury prevention. Occupational stress, a significant component of social psychology, has garnered widespread attention due to its potential impact on occupational injury. Objective To analyze the factors influencing occupational stress among cable manufacturing workers and explore the relationship between occupational stress and occupational injury, and to provide scientific evidence for reducing occupational stress and injury. Methods A questionnaire on basic demographics, occupational injury, and occupational stress (Effort-Reward Imbalance, ERI) was used to investigate 1343 workers in a cable enterprise. Chi-square test was employed to analyze the distribution of occupational stress across different worker characteristics. Logistic regression was used to identify factors influencing occupational stress, and a decision tree model was applied to assess the impact of occupational stress on injury risk. Results A total of 1225 valid questionnaires were collected, yielding a response rate of 91.2%. The positive rate of occupational stress was 28.7%, while the incidence of occupational injury in the past year was 8.6%. The results of univariate analysis revealed that workers who had experienced occupational injuries had a higher positive rate of occupational stress compared to those without injuries (P<0.05), and there were significant differences in the distribution of occupational stress by genders, overtime work, shift work, daily working hours, fatigue levels, sleep duration, family relationships, and colleague relationships (P<0.05). The logistic regression results showed that individuals who worked shifts, had fair/poor/very colleague relationships, and frequently worked overtime had a higher risk of occupational stress (P<0.05). The final decision tree model confirmed that occupational stress was a risk factor for occupational injury. Additional factors influencing occupational injury included physical exercise, colleague relationships, pre-job training participation, and gender. Workers who exercised less than twice per week and experienced occupational stress had a significantly higher risk of injury (23.6%) compared to those without occupational stress (10.9%). Conclusion Shift work, colleague relationships, and overtime work are correlated with the positive rate of occupational stress among cable manufacturing workers. Implementing a reasonable work schedule and fostering a positive work environment are recommended. Reducing occupational stress may help lower the risk of occupational injuries in this industry.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.

OBJECTIVE To summarize the current situation of pharmaceutical clinic service in our hospital over the past five years， and explore sustainable development strategies for service models of pharmaceutical clinics. METHODS A retrospective analysis was conducted on the consultation records of patients who registered and established files at the pharmaceutical clinic in our hospital from January 2019 to December 2023. Statistical analysis was performed on patients’ general information， medication- related problems， and types of pharmaceutical services provided by pharmacists. RESULTS A total of 963 consultation records were included， among which females aged 20-39 years accounted for the highest proportion （66.04%）； obstetrics and gynecology- related consultations accounted for the largest number of cases. Additionally， 80 patients attended follow-up visits at our hospital’s pharmaceutical clinic. A total of 1 029 medication-related issues were resolved， including 538 cases of drug consultations （52.28%）， 453 medication recommendations （44.02%）， 22 medication restructuring（2.14%）， and 16 medication education （1.55%）； the most common types of medication-related problems identified were adverse drug events（70.07%）. CONCLUSIONS Although the pharmaceutical clinic has achieved recognition from clinicians and patients， challenges such as low awareness among healthcare providers and the public persist. Future efforts should focus on strengthening information technology construction， enhancing pharmacist training， and establishing various forms of outpatient pharmaceutical service models.