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.

ObjectiveTo analyze the incidence and trend of stroke in Beilun District, so as to provide evidence for identifying influencing factors and reducing stroke incidence. MethodsStroke cases from 2012 to 2023 were extracted from the Ningbo Chronic Disease Collaborative Management System. Population information of Beilun District during the same period was also collected. The annual incidence and trends of stroke were analyzed. ResultsFrom 2012 to 2023, the age-standardized incidence rate of stroke in Beilun District, Ningbo City was 317.68/100 000, showing an increasing trend with an average annual percentage change (AAPC) of 2.267% (P=0.034). Among all subdistricts in Beilun District, two showed a downward trend in incidence, while the rest showed an upward trend. The crude incidence rate of stroke was significantly higher in males than that in females (P<0.001). The age-standardized incidence rate in males was 406.08/100 000, showing an increasing trend (AAPC=3.956%, P<0.001). The incidence of stroke also showed an increasing trend in the following age groups: 30‒<45 years (AAPC=6.340%, P=0.004), 45‒<60 years (AAPC=4.997%, P<0.001), and 60‒<75 years (AAPC=3.282%, P=0.042). Across all years, males had higher crude incidence rates in both ischemic and hemorrhagic stroke than females (P<0.05). The age-standardized incidence rate of ischemic stroke showed a rising trend in both males and the general population (male AAPC=4.905%, P<0.001; overall population AAPC=3.065%, P=0.001). ConclusionThe age-standardized incidence of stroke in Beilun District is on the rise, with higher crude incidence rate in males than that in females. The onset age of stroke is gradually declining. The age-standardized incidence rate of male ischemic stroke shows a clear upward trend.

Neurofibromatosis type 1 (NF1) presents with a diverse range of symptoms that can affect the skin, bones, eyes, central nervous system, and other organs. This article reports the diagnosis and treatment process of a patient with NF1 complicated by bilateral severe-to-profound sensorineural hearing loss. Genetic testing revealed a heterozygous variant of NF1 NM_ 000267.3: c.4054_ 4058del(p.Ser1352LeufsTer20, supporting the diagnosis of NF1. After thorough evaluation, a right cochlear implantation was performed, yielding satisfactory postoperative results. This case suggests that NF1 patients may exhibit phenotypic heterogeneity and atypicality, providing a reference for clinicians in the diagnosis and treatment of such patients.

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.

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.

To address the economic challenge associated with acquiring labeled sample data for white paeony,a semi-supervised learning model based on metric learning and consistency constraints was proposed to predict the content of trace heavy metal pollutants Pb and Cd in white paeony.The model was comprised of two multi-task deep learning networks with the same structure but different parameters,namely the teacher model and the student model.The multi-task deep learning network utilized metric learning within the classification branch to ensure the clustering of different samples,thereby enhancing the predictive performance of the regression branch for heavy metal content in white peony samples.The student model effectively utilized unlabeled data by constraining the consistency of outputs between the teacher and student models.Experimental results showed that the proposed multi-task deep learning network combined with the regression subnetwork model significantly reduced the average relative errors of Pb and Cd in the test set to 7.01%and 8.16%when predicting trace heavy metal pollutants in paeony.Furthermore,after integrating the metric learning loss function-constrained and the consistency-constrained teacher-student semi-supervised learning model,the same samples exhibited clustering phenomena,with faster convergence speed and convergence values closer to 0 in the loss function,reducing the average relative errors of Pb and Cd in the test set to 3.32%and 4.77%.The above results indicated that the model proposed in this work could effectively enhance the accuracy and reliability of LIBS in quantitative analysis of trace heavy metal elements in paeony,strengthening the advantages of LIBS in practical applications for quality supervision in the traditional Chinese medicine market.

Objective To analyze the characteristics of clinical indicators in patients with type 2 diabetes,and to establish a simple and effective risk prediction model for type 2 diabetes complicated with coronary heart disease by screening risk prediction indicators with machine learning.Methods A retrospective study was conducted,and 217 patients diagnosed with coronary artery disease combined with type 2 diabetes mellitus who were hospitalized in the Hospital from January 2022 to November 2023 were selected.Additionally,214 patients diagnosed with T2DM during the same period in the outpatient department were selected as the con-trol group.Their routine laboratory test data were recorded.The Least Absolute Shrinkage and Selection Op-erator(Lasso)algorithm was used to select features,and the models were built by using seven machine learn-ing algorithms:Random Forest,Decision Tree,Support Vector Machine,eXtreme Gradient Boosting,Logistic Regression,K-Nearest Neighbor,and Artificial Neural Network.The diagnostic efficacy of different models through receiver operating characteristic curve(ROC),area under curve(AUC),calibration curve,specificity,sensitivity,F1 value,and other indicators were evaluated.Results Twenty key factors,including age,gender,systolic blood pressure,diastolic blood pressure,heart rate,C-reactive protein and blood glucose were selected using Lasso regression.When incorporated into various models,the SVM model exhibited the highest sensitiv-ity(88.37％),negative predictive value(82.14％),and area under curve(0.845).The Random Forest model had the highest accuracy(76.47％),positive predictive value(76.74％),and F1 score(0.77).Meanwhile,the XGBoost algorithm demonstrated relatively good specificity(80.95％).After introducing the SHAP model,it was inferred that blood glucose had a significant positive impact on the occurrence of coronary heart disease in individuals with type 2 diabetes.Conclusion Machine learning can serve as an effective tool for assessing the risk of coronary heart disease in patients with type 2 diabetes.In this study,SVM,Random Forest,and XG-Boost models all demonstrate good predictive performance,indicating promising clinical application prospects.