i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.

BACKGROUND: 5-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key importance in genetic and pathological studies. However, traditional experimental methods for identifying 5mC sites are time-consuming and costly, so there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites. RESULTS: Deep learning methods have shown great potential in the field of 5mC sites, so we developed a deep learning combinatorial model called i5mC-DCGA. The model innovatively uses the Convolutional Block Attention Module (CBAM) to improve the Dense Convolutional Network (DenseNet), which is improved to extract advanced local feature information. Subsequently, we combined a Bidirectional Gated Recurrent Unit (BiGRU) and a Self-Attention mechanism to extract global feature information. Our model can learn feature representations of abstract and complex from simple sequence coding, while having the ability to solve the sample imbalance problem in benchmark datasets. The experimental results show that the i5mC-DCGA model achieves 97.02%, 96.52%, 96.58% and 85.58% in sensitivity (Sn), specificity (Sp), accuracy (Acc) and matthews correlation coefficient (MCC), respectively. CONCLUSIONS: The i5mC-DCGA model outperforms other existing prediction tools in predicting 5mC sites, and it is currently the most representative promoter 5mC site prediction tool. The benchmark dataset and source code for the i5mC-DCGA model can be found in https://github.com/leirufeng/i5mC-DCGA .

Synthesis and Assembly of Photoresponsive Colloidal Tubes.

Inspired by the sophisticated multicomponent and multistage assembly of proteins and their mixtures in living cells, this study rationally designs and fabricates photoresponsive colloidal tubes that can self-assemble and hybrid-assemble when mixed with colloidal spheres and rods. Time-resolved observation and computer simulation reveal that the assembly is driven by phoretic attraction originating from osmotic pressures. These pressures are induced by the chemical concentration gradients generated by the photochemical reaction caused by colloidal tubes in a H2O2 solution under ultraviolet (UV) irradiation. The assembled structure is dictated by the size and shape of the constituent colloids as well as the intensity of the UV irradiation. Additionally, the resulting assembly can undergo self-propelled motion originating from the broken symmetry of the surrounding concentration gradients. This motion can be steered by a magnetic field and used for microscale cargo delivery. The study demonstrates a facile synthesis method for colloidal tubes and highlights their unique potential for controlled, hierarchical self-assembly and hybrid-assembly.

Biologically active isoquinoline alkaloids covering 2019-2022.

Isoquinoline alkaloids are an important class of natural products that are abundant in the plant kingdom and exhibit a wide range of structural diversity and biological activities. With the deepening of research in recent years, more and more isoquinoline alkaloids have been isolated and identified and proved to contain a variety of biological activities and pharmacological effects. In this review, we introduce the research progress of isoquinoline alkaloids from 2019 to 2022, mainly in the part of biological activities, including antitumor, antimicrobial, antidiabetic, antiviral, anti-inflammatory, antioxidant, neuroprotective, hepatoprotective, analgesic, and other activities. This study provides a clear direction for the rational development and utilization of isoquinoline alkaloids, suggesting that these alkaloids have great potential in the field of drug research.

Servant leadership behaviour of head nurse assessment and its linkage with nurse work engagement in China.

AIM: To assess the servant leadership behaviour of head nurse and its linkage with nurse work engagement in China. DESIGN: A cross-sectional study. METHODS: A anonymous investigation with the stratified cluster randomized sampling of nurse was conducted in Hunan Province of China in December 2020. We administered the Perceived Head Nurse Service Leadership Behaviour Scale and the Chinese version of Utrecht work engagement scale to survey, and analyse its relationships by multiple linear regression. RESULTS: A number of 890 nurses participated in this study. The average score of the perceived servant leadership of head nurse reported by nurses was 78.90 ± 14.04, which was at a medium level. Among its six dimensions, the dimension of promote nurse development scored highest (16.04 ± 2.84), while the dimension of dedication scored lowest (11.39 ± 2.46). Official nurses reported higher perceived servant leadership scores of head nurses than those who were employed and temporary nurses (b = 1.727, 95% CI: 0.054-3.400); nurses in tertiary hospitals reported higher perceived servant leadership scores of head nurses than nurses in primary and secondary hospitals (b = 2.703, 95% CI: 0.305-5.100); and lower perceived servant leadership scores were associated with nurses' job overtime (b = -4.935, 95% CI: -6.891 to -2.978). Nurses' perceived servant leadership of head nurses were positively associated with nurses' work engagement (r = 0.336, p < .05). Multiple linear regression analysis indicated that the perceived servant leadership of head nurse affected nurses' work engagement strongly (b = 0.585, 95% CI: 0.479-0.691). CONCLUSION: The servant leadership behaviour of head nurse in China was at the medium level, which was positively associated with nurses' work engagement. Further research should improve the power of the servant leadership behaviour of head nurse by integrating additional training, policies and support. IMPACT: It is time to consider the servant leadership behaviour of head nurses and its linkage with nurses' work engagement in China seriously, and address the policies, guidelines, curriculum, and practice culture. PATIENT OR PUBLIC CONTRIBUTION: The study was conducted to explore the situation of servant leadership behaviour of head nurses and its linkage with nurses' work engagement in China, which did not include input from the public or the intended participants.

[Effect of comprehensive intervention on the diabetes-relevant behaviors in aging patients with prediabetes in rural areas of Yiyang].

OBJECTIVE: To explore the effect of comprehensive intervention on the diabetes-relevant behaviors in aging patients with prediabetes in rural areas of Yiyang.
 Methods: The aging patients with prediabetes in rural areas of Yiyang were chosen and asked for the questionnaire. The patients were randomly divided into two groups: the control group and the intervention group. The control group received the routine health education, while the intervention group underwent comprehensive intervention for one year. 6 months and 12 months before the intervention or after the intervention, the behavior characteristics of diabetes, levels of related physiological and biochemical indexes were evaluated.
 Results: The relative behavior characteristics of diabetes, levels of related physiological and biochemical indexes between the two groups were comparable before the intervention (P>0.05). 6 months or 12 months after the intervention, it showed improvements in diabetes-related behaviors (P<0.05) and the scores of diabetes-related behaviors compared with the control group (P<0.001). Based on the related physiological and biochemical indexes, the intervention group showed significant reductions in the levels of waist-to-hip ratio, BMI, fasting plasma glucose and 2 h postprandial plasma glucose (P<0.01).
 Conclusion: The comprehensive intervention can improve the diabetes-relevant behaviors, physiological and biochemical indexes in the older patients with pre-diabetes.

[Relationship between the ischemic ST-T changes in ECG and the coronary artery diseases].

OBJECTIVE: To analyze the relationship between the ischemic ST-T changes in electrocardiogram (ECG) and the coronary artery diseases based on the perspective of diagnostics.
 METHODS: A total of 341 patients, who underwent coronary angiography in Department of Cardiology of Xiangya Hospital, Central South University from June 2013 to April 2014, were enrolled for this study. The internationally recognized diagnostic criteria for ischemic ST-T changes in ECG and the Judkins diagnostic criteria for coronary angiography were applied, respectively. The sensitivity and specificity of ECG were analyzed.
 RESULTS: There were more ischemic ST-T changes in women than that in men (P<0.01). Ischemic changes in coronary angiography were not significantly different between male and female patients (P>0.05). For ischemic diagnostic tests by ECG ST-T, the total sensitivity and specificity was 83.6% and 54.4%, respectively. The sensitivity and specificity was 82.3% and 68.0% or 85.0% and 28.2% in the male or female group, respectively. 
 CONCLUSION: Ischemic ST-T changes in ECG possess important value in the diagnosis of the coronary artery diseases. The sensitivity of ECG in the diagnosis of myocardial ischemia in women was higher than that in men, whereas the specificity of ECG in the diagnosis of myocardial ischemia in men was higher than that in women.

iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.

The promoter is a key DNA sequence whose primary function is to control the initiation time and the degree of expression of gene transcription. Accurate identification of promoters is essential for understanding gene expression studies. Traditional sequencing techniques for identifying promoters are costly and time-consuming. Therefore, the development of computational methods to identify promoters has become critical. Since deep learning methods show great potential in identifying promoters, this study proposes a new promoter prediction model, called iPro2L-DG. The iPro2L-DG predictor, based on an improved Densely Connected Convolutional Network (DenseNet) and a Global Attention Mechanism (GAM), is constructed to achieve the prediction of promoters. The promoter sequences are combined feature encoding using C2 encoding and nucleotide chemical property (NCP) encoding. An improved DenseNet extracts advanced feature information from the combined feature encoding. GAM evaluates the importance of advanced feature information in terms of channel and spatial dimensions, and finally uses a Full Connect Neural Network (FNN) to derive prediction probabilities. The experimental results showed that the accuracy of iPro2L-DG in the first layer (promoter identification) was 94.10% with Matthews correlation coefficient value of 0.8833. In the second layer (promoter strength prediction), the accuracy was 89.42% with Matthews correlation coefficient value of 0.7915. The iPro2L-DG predictor significantly outperforms other existing predictors in promoter identification and promoter strength prediction. Therefore, our proposed model iPro2L-DG is the most advanced promoter prediction tool. The source code of the iPro2L-DG model can be found in https://github.com/leirufeng/iPro2L-DG.

A rare case of successful treatment of peritoneal dialysis patient with Serratia marcescens peritonitis without catheter removal: case report and literature review.

Serratia marcescens, as a Gram-negative opportunistic pathogen, is a rare cause of peritonitis and has worse clinical outcomes than Gram-positive peritonitis. In this case report, we describe a case of Serratia marcescens associated peritonitis that was successfully cured without catheter removal. A 40-year-old male patient with peritoneal dialysis who worked in the catering industry was admitted to the hospital for 16 hours after the discovery of cloudy peritoneal dialysate and abdominal pain. Ceftazidime and cefazolin sodium were immediately given intravenously as an empirical antibiotic regimen. After detecting Serratia marcescens in the peritoneal diasate culture, the treatment was switched to ceftazidime and levofloxacin. The routine examination of peritoneal dialysate showed a significant decrease in white blood cells, the peritoneal dialysate became clear, and the peritoneal dialysis catheter was retained. The patient was treated for 2 weeks and treated with oral antibiotics for 1 week. It is necessary to further strengthen the hygiene of work environment to prevent Serratia marcescens infection in peritoneal dialysis patients. We recommend that patients with Serratia marcescens associated peritonitis should be treated with a combination of antibiotics as early as possible empirically, and at the same time, the peritoneal dialysis fluid culture should be improved, and the antibiotic regimen should be timely adjusted according to the drug sensitivity results. For patients with clinical symptoms for more than 3 days, considering the strong virulence of Serratia marcescens, whether to use meropenem directly or not can provide a reference for clinical decision-making. Further clinical studies are needed to achieve more precise anti-infective treatment.

Risk factors of neuropathic pain in multiple sclerosis: a retrospective case-cohort study.

Background: Pain is a common symptom in multiple sclerosis (MS), especially neuropathic pain, which has a significant impact on patients' mental and physical health and quality of life. However, risk factors that related to neuropathic pain, still remain unclear. Objective: The study aimed to explore the risk factors of neuropathic pain among MS patients. Materials and methods: This retrospective study examined the consecutive patients diagnosed with MS in the Department of Neurology of Guangdong Provincial Hospital of Chinese Medicine between August 2011 and October 2022. Neuropathic pain was defined as "pain arising as a direct consequence of a lesion or disease affecting the somatosensory system". Demographic and clinical features were obtained from the electronic system of the hospital. Results: Our cohort revealed that the prevalence of patients with neuropathic pain in MS was 34.1%. The results indicated that the longer the spinal lesions, the greater the neuropathic pain risks (2-4: OR, 13.3(2.1-82), >5: OR, 15.2(2.7-86.8), p for tread: 0.037). Meanwhile, multivariate regression analysis showed that cervical and thoracic lesions (OR 4.276, 95% CI 1.366-13.382, P = 0.013), upper thoracic lesions (T1-T6) (OR 3.047, 95% CI 1.018-9.124, P = 0.046) were positively correlated with neuropathic pain, while basal ganglia lesions (OR 0.188, 95% CI 0.044-0.809, P = 0.025) were negatively correlated with neuropathic pain among MS patients. Conclusion: Extended spinal lesions (≥3 spinal lesions), cervical and thoracic lesions, upper thoracic lesions were independent risk factors of neuropathic pain among MS patients. Furthermore, our study found that the longer the spinal lesions, the greater the neuropathic pain risks.

DGA-5mC: A 5-methylcytosine site prediction model based on an improved DenseNet and bidirectional GRU method.

The 5-methylcytosine (5mC) in the promoter region plays a significant role in biological processes and diseases. A few high-throughput sequencing technologies and traditional machine learning algorithms are often used by researchers to detect 5mC modification sites. However, high-throughput identification is laborious, time-consuming and expensive; moreover, the machine learning algorithms are not so advanced. Therefore, there is an urgent need to develop a more efficient computational approach to replace those traditional methods. Since deep learning algorithms are more popular and have powerful computational advantages, we constructed a novel prediction model, called DGA-5mC, to identify 5mC modification sites in promoter regions by using a deep learning algorithm based on an improved densely connected convolutional network (DenseNet) and the bidirectional GRU approach. Furthermore, we added a self-attention module to evaluate the importance of various 5mC features. The deep learning-based DGA-5mC model algorithm automatically handles large proportions of unbalanced data for both positive and negative samples, highlighting the model's reliability and superiority. So far as the authors are aware, this is the first time that the combination of an improved DenseNet and bidirectional GRU methods has been used to predict the 5mC modification sites in promoter regions. It can be seen that the DGA-5mC model, after using a combination of one-hot coding, nucleotide chemical property coding and nucleotide density coding, performed well in terms of sensitivity, specificity, accuracy, the Matthews correlation coefficient (MCC), area under the curve and Gmean in the independent test dataset: 90.19%, 92.74%, 92.54%, 64.64%, 96.43% and 91.46%, respectively. In addition, all datasets and source codes for the DGA-5mC model are freely accessible at https://github.com/lulukoss/DGA-5mC.

im5C-DSCGA: A Proposed Hybrid Framework Based on Improved DenseNet and Attention Mechanisms for Identifying 5-methylcytosine Sites in Human RNA.

BACKGROUND: 5-methylcytosine (m5C) is a key post-transcriptional modification that plays a critical role in RNA metabolism. Owing to the large increase in identified m5C modification sites in organisms, their epigenetic roles are becoming increasingly unknown. Therefore, it is crucial to precisely identify m5C modification sites to gain more insight into cellular processes and other mechanisms related to biological functions. Although researchers have proposed some traditional computational methods and machine learning algorithms, some limitations still remain. In this study, we propose a more powerful and reliable deep-learning model, im5C-DSCGA, to identify novel RNA m5C modification sites in humans. METHODS: Our proposed im5C-DSCGA model uses three feature encoding methods initially-one-hot, nucleotide chemical property (NCP), and nucleotide density (ND)-to extract the original features in RNA sequences and ensure splicing; next, the original features are fed into the improved densely connected convolutional network (DenseNet) and Convolutional Block Attention Module (CBAM) mechanisms to extract the advanced local features; then, the bidirectional gated recurrent unit (BGRU) method is used to capture the long-term dependencies from advanced local features and extract global features using Self-Attention; Finally, ensemble learning is used and full connectivity is used to classify and predict the m5C site. RESULTS: Unsurprisingly, the deep-learning-based im5C-DSCGA model performed well in terms of sensitivity (Sn), specificity (SP), accuracy (Acc), Matthew's correlation coefficient (MCC), and area under the curve (AUC), generating values of 81.0%, 90.8%, 85.9%, 72.1%, and 92.6%, respectively, in the independent test dataset following the use of three feature encoding methods. CONCLUSIONS: We critically evaluated the performance of im5C-DSCGA using five-fold cross-validation and independent testing and compared it to existing methods. The MCC metric reached 72.1% when using the independent test, which is 3.0% higher than the current state-of-the-art prediction method Deepm5C model. The results show that the im5C-DSCGA model achieves more accurate and stable performances and is an effective tool for predicting m5C modification sites. To the authors' knowledge, this is the first time that the improved DenseNet, BGRU, CBAM Attention mechanism, and Self-Attention mechanism have been combined to predict novel m5C sites in human RNA.

Suicidal ideation of people living with HIV and its relations to depression, anxiety and social support.

BACKGROUND: The HIV/AIDS (human immunodeficiency virus/acquired immune deficiency syndrome) remains a global threat to health. Suicidal ideation has been a serious public health problem among people living with HIV (PLWH). However, the suicide prevention mechanism among PLWH still unclear. This study aims to analyze the suicidal ideation and its related factors in PLWH, and further explore the relationships between suicidal ideation and depression, anxiety and perceived social support. METHODS: This is a cross-sectional study. A total of 1146 PLWH were investigated by the general information questionnaire, the perceived social support scale (PSSS), the Beck scale for suicide ideation of Chinese version (BSI-CV), the generalized anxiety disorder scale-2 (GAD-2) and the patient health questionnaire-2 (PHQ-2) though the WeChat in China in 2018. By using statistical description and the binary unconditional logistic regression, we assessed the incidence of suicidal ideation and its related factors in PLWH. Besides, the intermediary effect of social support between anxiety, depression and suicidal ideation were explored by the stepwise test and Bootstrap method. RESULTS: The incidence of suicide ideation was 54.0% (619/1146) among the PLWH in the last week or during the most serious depression. Binary logistic regression analysis results showed that the PLWH who with short time for HIV positive diagnosis (aOR (adjusted odd ratio) = 1.754, 95% CI (confidence interval):1.338-2.299), low monthly income (aOR = 1.515, 95%CI:1.098-2.092), other chronic diseases except HIV (aOR = 1.555, 95%CI:1.134-2.132), irregular lovers (aOR = 1.369, 95%CI:1.021-1.837), anxiety (aOR = 2.711, 95%CI:1.767-4.161), depression (aOR = 1.614, 95%CI:1.078-2.417), low PSSS (aOR = 2.139, 95%CI:1.345-3.399) had high risk of suicide ideation.The social support played a mediating role between the anxiety (the mediating effect accounted for 30.43% of the total effect), depression (the mediating effect accounted for 23.76% of the total effect) and the suicide ideation among PLWH. CONCLUSION: The incidence of suicide ideation of PLWH was high. Anxiety, depression, and social support are the key factors of suicide ideation of PLWH. Social support plays a partial mediating role between anxiety, depression and suicidal ideation, which provides a new approach for prevention of suicidal ideation in PLWH and should be known widely for people to prevent suicide.

iEnhancer-DCSV: Predicting enhancers and their strength based on DenseNet and improved convolutional block attention module.

Enhancers play a crucial role in controlling gene transcription and expression. Therefore, bioinformatics puts many emphases on predicting enhancers and their strength. It is vital to create quick and accurate calculating techniques because conventional biomedical tests take too long time and are too expensive. This paper proposed a new predictor called iEnhancer-DCSV built on a modified densely connected convolutional network (DenseNet) and an improved convolutional block attention module (CBAM). Coding was performed using one-hot and nucleotide chemical property (NCP). DenseNet was used to extract advanced features from raw coding. The channel attention and spatial attention modules were used to evaluate the significance of the advanced features and then input into a fully connected neural network to yield the prediction probabilities. Finally, ensemble learning was employed on the final categorization findings via voting. According to the experimental results on the test set, the first layer of enhancer recognition achieved an accuracy of 78.95%, and the Matthews correlation coefficient value was 0.5809. The second layer of enhancer strength prediction achieved an accuracy of 80.70%, and the Matthews correlation coefficient value was 0.6609. The iEnhancer-DCSV method can be found at https://github.com/leirufeng/iEnhancer-DCSV. It is easy to obtain the desired results without using the complex mathematical formulas involved.

Psychological Resilience of Chinese College Students: A Cross-Sectional Study after the Deblocking of China's COVID-19 Pandemic Strategy.

Psychological resilience (PR) plays an important role in fortifying mental health during pandemics. This study aimed to examine the PR and its related factors of college students in China after the deblocking of the China's COVID-19 pandemic strategy. A total of 1100 college students from 15 universities participated in this cross-sectional survey by multi-stage stratified sampling. Data were collected by self-designed socio-demographic information, the family function assessment scale (APGAR), a general health questionnaire (GHQ-12), the general self-efficacy scale (GSES), and a psychological resilience scale. The average score of PR was 135.65 ± 18.54. Cluster analysis of PR scores showed that 24.9% of the college students had weak PR. The higher PR, the higher mental health status (r = 0.352, p < 0.05). Females had higher PR than males (OR = 0.550, 95% CI: 0.367-0.827). High self-efficacy was an independent protective factor of high PR (OR = 0.093, 95% CI: 0.059-0.145). Low family contact frequency, poor family function, and bad mental health status were the independent risk factors of high PR. In conclusion, the PR of Chinese college students were insufficient after the deblocking of China's COVID-19 pandemic strategy, indicating an improvement of PR should be put into practice immediately. Frequent monthly contact with family, family function, self-efficacy, mental health status, and gender were the influencing factors of PR, which provides an intervention strategy for the future.

The Fully Mediating Role of Psychological Resilience between Self-Efficacy and Mental Health: Evidence from the Study of College Students during the COVID-19 Pandemic.

Student populations are susceptible to the COVID-19 pandemic and may easy develop mental health problems related to their immaturity of psychological development and fluctuation of mood. However, little has been known about the effects of the pandemic on college students and the associated influencing factors. This study aimed to explore the role of psychological resilience as a mediator between general self-efficacy and mental health. A cross-sectional survey was conducted with 480 Chinese college students from 12 universities in Hunan province of China. The participants responded anonymously to the Generalized Self-Efficacy Scale (GSES), the Chinese version of the Resilience Scale for College Students (RSCS), and the 12-item General Health Questionnaire (GHQ-12). Hierarchical linear regression and structural equation modeling were used in this study. The average of GSES and RSCS scores of college students were 25.00 ± 4.68 and 137.97 ± 15.50, which were at a medium level. The average score for the GHQ-12 was 1.59 ± 1.59, and 22.03% of the college students scored ≥ 3 on the GHQ-12, indicating that they were at risk of developing mental disorders. According to the analyses of mediation effect, psychological resilience played a fully mediating role in the relationship between general self-efficacy and mental health. In conclusion, Chinese college students were at high risk of developing mental disorders during the COVID-19 period. General self-efficacy was positively associated with psychological resilience, and psychological resilience played a fully mediating role in the relationship between general self-efficacy and mental health. Future studies and interventions should aim to promote psychological resilience and general self-efficacy.

Plasma lipidomic profiling of thiopurine-induced leukopenia after NUDT15 genotype-guided dosing in Chinese IBD patients.

Introduction: Thiopurines, azathiopurine (AZA) and mercaptopurine (6-MP) have been regularly used in the treatment of inflammatory bowel disease (IBD). Despite optimized dosage adjustment based on the NUDT15 genotypes, some patients still discontinue or change treatment regimens due to thiopurine-induced leukopenia. Methods: We proposed a prospective observational study of lipidomics to reveal the lipids perturbations associated with thiopurine-induced leukopenia. One hundred and twenty-seven IBD participants treated with thiopurine were enrolled, twenty-seven of which have developed thiopurine-induced leucopenia. Plasma lipid profiles were measured using Ultra-High-Performance Liquid Chromatography-Tandem Q-Exactive. Lipidomic alterations were validated with an independent validation cohort (leukopenia n = 26, non-leukopenia n = 74). Results: Using univariate and multivariate analysis, there were 16 lipid species from four lipid classes, triglyceride (n = 11), sphingomyelin (n = 1), phosphatidylcholine (n = 1) and lactosylceramide (n = 3) identified. Based on machine learning feature reduction and variable screening strategies, the random forest algorithm established by six lipids showed an excellent performance to distinguish the leukopenia group from the normal group, with a model accuracy of 95.28% (discovery cohort), 79.00% (validation cohort) and an area under the receiver operating characteristic (ROC) curve (ROC-AUC) of 0.9989 (discovery cohort), 0.8098 (validation cohort). Discussion: Our novel findings suggested that lipidomic provided unique insights into formulating individualized medication strategies for thiopurines in IBD patients.

Prevalence and Risk Factors Underlying Occupational Stress and Depression Among Clinical Nurses in Secondary and Tertiary Hospitals of China During COVID-19 Setting.

Aim: This study was designed to develop an effective measurement tool for occupational stress among medical staff and to identify the underlying risk factors among clinical nurses in China under depression during and after COVID-19. Methods: In the first stage, an occupational stress scale was developed for medical staff based on qualitative and quantitative methods. The dimensions of the scale were based on childhood stress and seven other parameters of working stress. In the second stage, a provincial survey was conducted among clinical nurses in Hainan. The structure of Medical Staff Occupational Stress Scale was tested in secondary and tertiary hospitals. The socio-demographic information, occupational stress (measured using the developed scale), and current depression symptoms (assessed with the nine-item Patient Health Questionnaire) were evaluated. The risk factors for occupational stress-induced depression were tested using multivariate logistic regression. Results: The Medical Staff Occupational Stress Scale consisted of 42 items under eight dimensions with strong reliability and validity. Almost 80% of the clinical nurses reported obvious symptoms of depression. Based on multivariate logistical regression analysis, the significant risk factors for depression in nurses at secondary hospitals and tertiary hospitals were childhood stress, teaching stress, relationship with patient stress, and administration stress. Conclusion: The Medical Staff Occupational Stress Scale utilized in nursing population is based on strong psychometric features. Childhood stress contributes to occupational stress in nurses. The selection of nurses for clinical work may require evaluation of past history for childhood stress to prevent occupational depression. Teaching stress, relationship with patient stress and administration stress play significant roles in the prevention of depression among clinical nurses.

The Effect of Learning Burnout on Sleep Quality in Primary School Students: The Mediating Role of Mental Health.

Due to the growth of research on sleep, mental health, and learning burnout on healthy growth and its related public health significance of adolescents, this study aimed to provide a deeper understanding of the effect of mental health and learning burnout on sleep among primary school students. The sleep quality (subjective sleep quality, sleep time, sleep latency, sleep duration, sleep efficiency, sleep disturbance, and daytime dysfunction), mental health, and learning burnout (exhaustion, learning cynicism, and reduced efficacy) of 900 students of grades 3-6 in primary schools were assessed in 2020. The PSQI scores of participants were 4.19 ± 2.545, of which a number of 322 (39.03%) students had sleep disturbance (PSQI scores ≧ 5). Binary logistic regression analysis showed that screen time (OR = 1.518, 95% CI: 1.164-1.980), ranking status (OR = 0.659, 95% CI: 0.480-0.907), learning burnout (OR = 1.088, 95% CI: 1.067-1.108), and mental health (OR = 4.672, 95% CI: 1.954-11.173) were the influencing factors for sleep quality of grade 3-6 students. According to the mediation effect analyses, mental health played a mediating effect (58.73% of the total effect) on the relationship between learning burnout and sleep quality. In conclusion, primary school students in Hunan of China have prominent sleep problems, and the daytime dysfunction caused by sleep problems is the most serious. Learning burnout positively predicted poorer sleep quality, and mental health played a mediating effect on the relationship between learning burnout and sleep quality.

Patient-Perceived Health System Responsiveness of the Epilepsy Management Project in Rural China during the Period of COVID-19.

Health system responsiveness (HSR) measures the experience of health-system users in terms of the non-clinical domains of the health system, which has been regarded as the three major goals of health performance evaluation. Good HSR may promote the use of health services and ultimately the health of patients. However, the HSR has not been measured as the main goal of the Epilepsy Management Project (EMP) in rural China. This study aims to evaluate the levels and distributions of the patient-perceived HSR of the EMP in rural China during the period of COVID-19 and identify its relevant factors so as to provide advice on the improvement of further strategies. Based on the key informant survey (KIS) of responsiveness from the World Health Organization proposal, we conducted a cross-sectional survey of 420 epilepsy patients selected proportional randomly from seven rural areas in the Hunan province of China in 2021. Eight domains of patients-perceived HSR were assessed by face-to-face interview. The overall HSR scored at a fairly "good" level of 8.3 (8.3 out of a maximum of 10.0). During the COVID-19 period, the scores of responsiveness domains were highest at 8.66 to 8.93 in "confidentiality", "dignity" and "choice of providers", while lowest at 8.38 to 8.53 in "prompt attention", "social support" and "basic amenities". The representative responsiveness equality index (REI) was 0.732, indicating the moderately balanced distributions of responsiveness of the EMP in rural China. Female, old age, and low education were significantly related to the lower HSR scores of rural EMP (p < 0.05). The HSR of EMP in rural China was fairly good. However, measures to improve the patient-perceived HSR are still needed, especially including better service, higher social support, and more comfortable medical environments.

Analysis of work willingness and its influencing factors of mental illness prevention and control personnel of grassroots institutions in China: a cross-sectional study.

BACKGROUND: In China, there are many studies focusing on the willingness of general practitioners and special medical personnel to work. However, there is a lack of studies on the working willingness of mental illness prevention and control staff. METHODS: This is a cross-sectional study. In this study we selected mental illness prevention and control personnel in the grassroots health service institutions in Hunan Province, China as our subjects. The χ2 test and binary logistic regression analysis were used to explore their work willingness and related influencing factors. RESULTS: The work willingness of mental illness prevention and control staff of grassroots institutions in China was 55.6% (502/903). The influencing factors were age (odds ratio [OR] 0.753 [95% confidence interval {CI} 0.638 to 0.888]), educational background (OR 1.342 [95% CI 1.092 to 1.648]) and major (OR 1.083 [95% CI 1.009 to 1.162]). CONCLUSIONS: The work willingness of the mental illness prevention and control staff of grassroots institutions in China was at a low level. The job of preventing and controlling mental illness in grassroots areas needs the competence of workers with strong specialty and professional competence and workers who are willing to work in these communities. Work willingness is the result of multiple factors, especially healthcare investments.