Background As a pillar industry in China, the manufacturing sector has a high incidence of non-fatal occupational injuries. The factors influencing non-fatal occupational injuries in this industry are closely related at various levels, including individual, equipment, environment, and management, making the analysis of these influencing factors complex. Objective To identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. Methods A total of 2243 frontline workers from cable and shipbuilding enterprises were selected as study subjects to investigate the incidence of non-fatal occupational injuries and collect information at four levels: individual, equipment, management, and environment in past 12 months. Data balancing was performed using resampling, and LASSO regression was used to select factors of non-fatal occupational injuries. The influence degree and type of variables were judged based on the magnitude of the estimated coefficients of each variable, where variables with estimated coefficients > 0 are risk factors, and those <0 are protective factors. The area under the receiver operating characteristic (ROC)curve (AUC) was used to test the performance of the model, with an AUC value > 0.7 indicating good model performance. Results Among the 2243 frontline workers, males accounted for 77.7% (1742 out of 2243), with the main age range being 40-49 years old, representing 29.5% (661 out of 2243), 82.7% of the workers (1854 out of 2243) were married, and 55.6% (1248 out of 2243) had a junior middle school education level. The average monthly income for 51.0% (1144 out of 2243) of the workers was between 5000 and 6999 Chinese Yuan. The incidence of non-fatal occupational injuries among the manufacturing workers was 8.4% (189/2243) in the past 12 months. Among the 22 factors associated with the occurrence of non-fatal occupational injuries (P<0.05), 10 were individual-level factors, including gender, smoking, alcohol consumption, colleague relationships, average exercise duration, job burnout, work fatigue, musculoskeletal disorders, cardiovascular diseases, and neurological and sensory organ diseases; 3 were equipment-level factors, including equipment operability, hazardous workpieces, and safety hazards; 5 were environmental-level factors, including low temperatures, special operations, noise, workspace size, and dirty and disorderly environment; and 4 were management-level factors, including daily working hours, weekly working days, overtime, and pre-job technical training. The AUC value of the LASSO regression model was 0.704 and the final model retained a total of 10 variables. Among them, there were 7 risk factors for non-fatal occupational injuries (coefficient > 0), including safety hazards, musculoskeletal disorders, dangerous workpieces, job burnout, dirty and disorderly environment, smoking, and male gender; and 3 protective factors (coefficient < 0), including pre-job technical training, good colleague relationship, and long working days per week. Conclusion Manufacturing enterprises need to focus on the incidence of non-fatal occupational injuries and conduct targeted interventions for non-fatal occupational injuries by controlling potential safety hazards, providing pre-job technical training, reducing dangerous workpieces, rectifying working environment, and reasonably arranging working hours.

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.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

ObjectiveTo characterize the seasonal patterns of influenza in Kunming City, Yunnan Province before, during, and after the COVID-19 pandemic, and provide scientific evidence for optimizing influenza prevention and control strategies. MethodsInfluenza-like illness (ILI) and etiological surveillance data for influenza from the 14^th week of 2010 to the 13^th week of 2024 in Kunming City of Yunnan Province were collected. Harmonic regression models were constructed to analyze the epidemic characteristics and seasonal patterns of influenza before (2010/2011‒2019/2020 influenza seasons), during (2020/2021‒2022/2023 influenza seasons), and after (2023/2024 influenza season) the COVID-19 pandemic. ResultsBefore the COVID-19 pandemic, influenza in Kunming City mainly exhibited an annual cyclic pattern without a significant semi-annual periodicity, peaking from December to February of the next year, with an epidemic duration of 20‒30 weeks. During the pandemic, influenza seasonality shifted, with an increase in semi-annual periodicity and an approximate one month delay in annual peaks. However, after the pandemic, the annual amplitude of influenza increased compared with that before the pandemic, and the epidemic duration extended by about one month. Although the annual peak largely reverted to the pre-pandemic levels, the annual peaks for different influenza subtypes/lineages had not fully recovered. ConclusionInfluenza seasonality in Kunming City underwent substantial alterations following the COVID-19 pandemic and has not yet fully reverted to pre-pandemic levels. Continuous surveillance on different subtypes/lineages of influenza viruses remains essential, and prevention and control strategies should be adjusted and optimized in a timely manner based on current epidemic trends.

Objective:To explore the risk factors for peripherally inserted central catheter (PICC) related bloodstream infections in elderly acute leukemia patients during chemotherapy, and construct and validate relevant predictive models.Methods:This study selected 291 patients from the First Affiliated Hospital of Zhengzhou University from January 2019 to December 2020 as the modeling group, and 277 patients from January 2021 to December 2022 as the validation group. Multivariate Logistic regression was used to determine the risk factors for PICC related bloodstream infections during chemotherapy in elderly acute leukemia patients, and a prediction model was constructed and validated based on the analysis results.Results:The incidence of PICC related bloodstream infections in the modeling group was 8.2% (24/291). Multivariate Logistic regression analysis showed that chemotherapy frequency, single catheterization puncture frequency, whether catheterization maintenance frequency was standardized, and catheterization retention time were risk factors for PICC related bloodstream infections in elderly acute leukemia patients during chemotherapy. Based on this result, a prediction model was constructed, and the area under the receiver operating characteristic curve ( AUC) of the modeling group's prediction model was 0.798 [95% CI (0.734, 0.869) ], while the AUC of the validation group's prediction model was 0.745 [95% CI (0.712, 0.844) ]. The calibration curves of the modeling and validation groups showed that the prediction model had high predictive performance. Conclusions:A prediction model for PICC related bloodstream infections in elderly acute leukemia patients during chemotherapy is constructed based on the results of multiple factor analysis, and the predictive performance of the model is verified. Nursing staff can quantify the risk of PICC related bloodstream infections in patients based on this prediction model and implement targeted nursing measures.

Objective:To understand the current situation of adolescent depression research in China and explore the research hotspots and development trends, so as to provide reference for adolescent depression related research.Methods:The literature related to adolescent depression in China National Knowledge Infrastructure was retrieved from the establishment of the database until February 25, 2023. CiteSpace software was used for visual analysis of the literature.Results:A total of 2 739 articles were included, and the number of published papers showed an increasing trend from 1980 to 2023. The author with the most articles was Zhang Wenxin and the institution with the largest number of publications was Chongqing Medical University. The research hotspots mainly focused on self-esteem, redundancy, social support, family environment, peer relationships and other aspects and research trends included parental conflict, peer relationships, self-injury, rumination and so on.Conclusions:The problem of adolescent depression has attracted widespread attention from scholars, and it is necessary to strengthen the communication and cooperation between authors and institutions in the future, carry out more interdisciplinary research, so as to provide more help for the intervention and treatment of adolescent depression.

Objective:To systematically evaluate the bias risk and applicability of short-term mortality risk prediction models within 30 days after stroke, providing a basis for selecting or developing standardized risk prediction models.Methods:Research on short-term mortality risk prediction models within 30 days after stroke was electronically retrieved from China National Knowledge Infrastructure, WanFang Data, VIP, and China Biomedical Database, PubMed, Web of Science, Embase, Cochrane Library and CINAHL. The search period was from database establishment to December 5, 2023. Two researchers independently conducted literature screening and quality evaluation.Results:Twelve studies were included, and a total of 31 models were internally validated, with 7 models undergoing external validation based on internal validation. 26 models reported discriminative power, and 18 models reported calibration methods. The most frequent predictors of modeling were age, hypertension, atrial fibrillation, diabetes and admission Glasgow Coma Scale score. Due to methodological problems such as insufficient sample size, improper handling of missing variables, and inadequate reporting of modeling information, all included studies were rated as high risk of bias.Conclusions:The research on short-term mortality risk prediction models for stroke patients is still in the development stage. Although it has good applicability, the risk of bias is relatively high. Future research should be designed and reported based on prediction model risk of bias assessment tool (PROBAST) and transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) to avoid common problems summarized in this study and reduce the risk of bias.

Sialic acid-binding immunoglobulin-like lectins(Siglecs)are expressed on most white blood cells and play a key role in signal transduction of immune cells.Recent studies have shown that many bacterial pathogens regulate the host immune response through the interaction between sialic acid(Sia)and Siglecs,thereby influencing the occurrence and development of diseases.Understanding bacterial pathogenic mechanisms is essential to identify potential effective therapeutic targets and help manage bacterial infectious diseases.Herein,the structure,biological function,and research progress in Siglecs in bacterial in-fectious diseases are briefly reviewed.

Objective The researchers systematically retrieved,evaluated,and summarized the best evidence for the care of nebulized inhalation in adult patients on mechanical ventilation,to provide a basis for standardizing the care of nebulized inhalation in patients on mechanical ventilation.Methods We systematically searched the domestic and foreign databases to collect the evidence on the literature related to nebulization therapy for mechanical ventilation in adults.The time for the retrieval is from the inception of databases until February 2023.There were 3 researchers who evaluated the quality of the literature,and extracted and summarized the evidence based on this subject.Results A total of 19 articles were obtained in database.42 pieces of evidence were summarized,covering pre-assessment of nebulized inhalation,preparation before nebulized inhalation,medication management,selection and standardized use of nebulization devices,respiratory machine mode and parameter settings,equipment management during nebulized inhalation,evaluation of effect,management of adverse reactions,disposal of materials and environment after nebulized inhalation and management of nebulized inhalation for respiratory infectious diseases.Conclusion This study summarized the best evidence for nebulized inhalation nursing in adult patients with mechanical ventilation,so as to provide a reference of standardized nebulized inhalation therapy for such patients,which is conducive to ensuring the safety of patients.