Optimizing the detection of emerging infections using mobility-based spatial sampling.

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.

Perceived social support mediates the effect of COVID-19 pandemic on job adaptation disorders of workers: An exploratory cross-sectional study.

COVID-19 lockdown can lead to job adaptation disorders, which are heterogeneous among individuals. The purpose of this study was to explore the association between perceived social support and job adaptation disorders among workers in China during the COVID-19 pandemic. The questionnaires of Psychological Questionnaire for Public Health Emergencies, Multidimensional Scale of Perceived Social Support, Work Attitude Scale were used for this cross-section study via an online survey. The study included 626 employees. Hierarchical regression analysis and Bootstrap method were used to investigate the mediation effect of perceived social support between the emergency and job adaptation disorders. The percentages of the 5 dimensions of depression, neurasthenia, fear, compulsion-anxiety, and hypochondria in workers were 59.7%, 56.1%, 92.3%, 42.0%, and 18.7%, respectively. Social support mediated the relationship between depression, neurasthenia, obsessive-compulsive anxiety and job adaptation disorder, accounting for 18.1%, 16.1%, and 17.5% of the total effect (ab/c), respectively. Perceived social support could alleviate COVID-19 pandemic-related depression, neurasthenia, compulsion-anxiety, and job adaptation disorder in Chinese workers. Improving their perception of social support, workers may better adapt themselves to work in the challenging of the public health emergency during COVID-19 pandemic.

CartiMorph: A framework for automated knee articular cartilage morphometrics.

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient ρ∈[0.82,0.97]), surface area (ρ∈[0.82,0.98]) and volume (ρ∈[0.89,0.98]) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.

Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification.

Background: Osteoarthritis (OA) is a global healthcare problem. The increasing population of OA patients demands a greater bandwidth of imaging and diagnostics. It is important to provide automatic and objective diagnostic techniques to address this challenge. This study demonstrates the utility of unsupervised domain adaptation (UDA) for automated OA phenotype classification. Methods: We collected 318 and 960 three-dimensional double-echo steady-state magnetic resonance images from the Osteoarthritis Initiative (OAI) dataset as the source dataset for phenotype cartilage/meniscus and subchondral bone, respectively. Fifty three-dimensional turbo spin echo (TSE)/fast spin echo (FSE) MR images from our institute were collected as the target datasets. For each patient, the degree of knee OA was initially graded according to the MRI Knee Osteoarthritis Knee Score before being converted to binary OA phenotype labels. The proposed four-step UDA pipeline included (I) pre-processing, which involved automatic segmentation and region-of-interest cropping; (II) source classifier training, which involved pre-training a convolutional neural network (CNN) encoder for phenotype classification using the source dataset; (III) target encoder adaptation, which involved unsupervised adjustment of the source encoder to the target encoder using both the source and target datasets; and (IV) target classifier validation, which involved statistical analysis of the classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. We compared our model on the target data with the source pre-trained model and the model trained with the target data from scratch. Results: For phenotype cartilage/meniscus, our model has the best performance out of the three models, giving 0.90 [95% confidence interval (CI): 0.79-1.02] of the AUROC score, while the other two model show 0.52 (95% CI: 0.13-0.90) and 0.76 (95% CI: 0.53-0.98). For phenotype subchondral bone, our model gave 0.75 (95% CI: 0.56-0.94) at AUROC, which has a close performance of the source pre-trained model (0.76, 95% CI: 0.55-0.98), and better than the model trained from scratch on the target dataset only (0.53, 95% CI: 0.33-0.73). Conclusions: By utilising a large, high-quality source dataset for training, the proposed UDA approach enhances the performance of automated OA phenotype classification for small target datasets. As a result, our technique enables improved downstream analysis of locally collected datasets with a small sample size.

The mediating effect of psychological flexibility on fatigue and depressive symptoms among nursing staff.

Objectives: To explore the relationship between depressive symptoms, fatigue and psychological flexibility, as well as their interactions on depression in Chinese nurses. Material and Methods: Using convenience sampling, a cross-sectional survey of 796 nurses in municipal hospitals of Zhengzhou, Henan Province, China, was conducted. The questionnaires of Work-related Acceptance and Action Questionnaire, Center for Epidemiological Studies Depression Scale and Fatigue Assessment Instrument were used. Hierarchical regression and bootstrap methods were used to examine the mediating effect of psychological flexibility between fatigue and depression. Results: More than 51.8% of the nurses were at risk of depression and 62.3% were at risk of fatigue. There was a significantly positive and moderate correlation between depression and fatigue severity, situation specificity, and consequences (r = 0.43, r = 0.24 and r = 0.31, respectively, p < 0.01). Depression was negatively correlated with psychological flexibility (r = -0.28, p < 0.01). Psychological flexibility had a negative impact on depression with the explained variance increased by 4.2% (ß = -0.211, p < 0.001). The bootstrap method showed that the mediating effect of psychological flexibility accounting for 8.5% and 12.3% on fatigue and depressive symptoms, respectively. Conclusions: Psychological flexibility plays a partial mediating role between the fatigue severity, consequences of fatigue and depressive symptoms of nurses. Hospital managers should improve medical staff work acceptance to alleviate their depressive symptoms. Int J Occup Med Environ Health. 2023;36(4):563-74.

Optimizing the detection of emerging infections using mobility-based spatial sampling.

Background: Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.

Effects of public-health measures for zeroing out different SARS-CoV-2 variants.

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.

Psychological Capital and Self-Acceptance Modified the Association of Depressive Tendency with Self-Rated Health of College Students in China during the COVID-19 Pandemic.

BACKGROUND: To explore the association between the self-reported health status, depressive tendency, psychological capital, and self-acceptance of college students in China during the COVID-19 pandemic. METHODS: Using the online survey platform "questionnaire star", a two-phase cross-sectional study was conducted on a total number of 1438 undergraduates with informed consents. The questionnaires of Self-Rated Health Measurement Scale (SRHMS), the Center for Epidemiological Studies-Depression Scale (CES-D), Psychological Capital Questionnaire (PCQ-24), and self-acceptance questionnaire were administered to each participant. RESULTS: Male college students had significantly higher depressive tendency scores than female (17.59 vs. 15.82) (p < 0.01). College students having no siblings had significantly higher psychological capital scores than those having siblings (108.63 vs. 105.60) (p < 0.05). Exercise had significantly positive associations with self-rated health, psychological capital, and self-acceptance scores, while online time per day had significantly negative associations. Multivariate analysis showed that the interaction between depressive tendency, psychological capital, and self-acceptance was statistically significant (ß = 0.004, p = 0.013 for phase 1 and ß = 0.002, p = 0.025 for phase 2) in health status with depressive tendency ranking the top (ß = -0.54 for phase 1 and -0.41 for phase 2, p < 0.001). Mediation analysis showed that psychological capital and self-acceptance modified the association of depressive tendency with health status. CONCLUSION: Physical exercise is beneficial to both physical and psychological health. Depressive tendency is the main risk factor that associates with self-rated health. Regardless of depressive tendency level, high psychological capital and self-acceptance could improve college students' health.

Interaction of job-related psychological flexibility, coping style and personality types in depression in Chinese physicians: A cross-section study.

To investigate the associations of job-related psychological flexibility, coping style and personality types with and their interactions in depression in Chinese physicians. A cross-sectional survey of 444 physicians was conducted by using the convenience sampling method in the municipal hospitals in Zhengzhou, Henan province. Center for Epidemiological Studies Depression, Work-related Acceptance and Action Questionnaire, the Simplified Coping Style Questionnaire and Eysenck Personality Questionnaire-Revision Short Scale of China were administered to each participant. Depression tendency scores were significantly higher in healthcare workers with intermediate title, age 31 and older, introvert unstable personality than other counterparts, (P < .01). Female and extrovert stable healthcare workers had significantly higher coping score than male and other personality types (P < .05). The scores of job-related psychological flexibility in healthcare workers with Introvert Stable or working in emergency department were significantly higher than their counterparts (P < .01). General linear model algorithm of machine learning showed that Extrovert Unstable was the main risk factor for depression (ß = 6.74), followed by Extrovert Stable (ß = -4.90), negative coping, positive coping, and length of service. Multivariate regression models showed that a significant interaction existed between coping style, work-related psychological flexibility and Extroversion (ß = -0.103, P < .05), independently explaining 0.7% variance of depression, and that a significant interaction existed between coping style, work-related psychological flexibility and neuroticism (ß = 0.116, P < .05), independently explaining 1.0% variance of depression. Interactions existed between personality types, coping style and work-related psychological flexibility in depression tendency in Chinese healthcare workers, with neuroticism (extrovert unstable) being a risk factor and extroversion (extrovert stable) being a protective factor. Precision prevention strategies could be made based on personality types to reduce depression in health workers.

Associations of Psychological Capital, Coping Style and Emotional Intelligence with Self-Rated Health Status of College Students in China During COVID-19 Pandemic.

Background: COVID-19 lockdown can lead to mental health problem, and the problem is heterogeneous across individuals. In this study, we aimed to explore the association between the self-reported health status, emotional coping style, emotional intelligence and positive psychological state of college students during the COVID-19 pandemic in China. Methods: The questionnaires of Self-rated Health Measurement Scale (SRHMS), Emotional Intelligence Scale (EIS), Psychological Capital Questionnaire (PCQ), Simplified Coping Style Questionnaire (SCSQ) were used for online survey. The study included 367 undergraduates. Results: Undergraduate sex, sibling and birth of place were associated with either psychological capital scores or emotional intelligence. Online time per day had significantly negative associations with self-rated health and psychological capital scores. Multivariate analysis showed that the interaction between emotional intelligence, psychological capital and coping styles was statistically significant (ß = -0.112, p = 0.045) in health status with psychological capital ranking the top (ß = 0.428). Mediation analysis showed that psychological capital and coping style modified the effect of emotional intelligence on health status. Conclusion: Psychological capital and coping style modified the effect of emotional intelligence on health status in college students during the COVID-19 pandemic. In this pandemic emergency, a comprehensive intervention, such as positive coping styles with optimistic emotional perception and understanding as well as social support, is an important measure against the pandemic-induced mental health problem in college students in China.

Personality modifies the effect of post-traumatic stress disorder (PTSD) and society support on depression-anxiety-stress in the residents undergone catastrophic flooding in Henan, China.

BACKGROUND: To analyze the impact of the flood disasters, social support and personality on the mental health of residents in Henan Province, China, providing fundamental knowledges for making measuring strategies to improve the psychological protection and anti-stress ability of the residents after the disaster. MATERIAL AND METHODS: A cross-section study was conducted via an online survey platform "questionnaire star," which included 572 residents in Henan Province, which underwent the history of ever flood disaster on July 20. The questionnaires of Impact of Event Scale-Revised Edition (IES-R), Perceived Social Support Scale (PSSS), the Depression Anxiety Stress Scales (DASS-21) and the scales of Eysenck Personality Questionnaire-Revised (EPQ-R) version in Chinese were also administered to each participant. Generalized linear regression model was performed. RESULTS: The residents who live in the flooding areas, are male and married had a significantly higher post-traumatic stress disorder (PTSD) score than their counterparts. The scores of depression-anxiety-stress in the residents with stable emotion were significantly lower than those with unstable emotion (p < 0.001). Machine learning showed that PTSD ranked the top risk factor, followed by neuroticism for Depression-Anxiety-Stress after disaster. The PTSD was negatively correlated with social support (p < 0.01), while it was positively correlated with depression-anxiety-stress and emotional stability (p < 0.01). There was a statistically significant interaction between PTSD, social support and neuroticism on depression-anxiety-stress (p < 0.001), with an independent effect of 1.4% on depression-anxiety-stress. Emotional stability showed the largest association with depression-anxiety-stress. CONCLUSIONS: Residents living in the catastrophic flooding areas had significant post-traumatic mental health issues, and the severity of mental problems was differently affected by post-traumatic stress disorder and social support in individuals with different personalities. Introvert and PTSD were the major risk factors for depression-anxiety-stress after the disaster. Med Pr. 2022;73(4):305-14.

Deep collaborative network with alpha matte for precise knee tissue segmentation from MRI.

BACKGROUND AND OBJECTIVE: Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), being state of the art, often challenged by the lack of image-specific adaptation, such as low tissue contrasts and structural inhomogeneities, thereby leading to incomplete segmentation results. METHODS: This paper presents a deep learning-based automatic segmentation framework for precise knee tissue segmentation. A novel deep collaborative method is proposed, which consists of an encoder-decoder-based segmentation network in combination with a low rank tensor-reconstructed segmentation network. Low rank reconstruction in MRI tensor sub-blocks is introduced to exploit the morphological variations in knee tissues. To model the tissue boundary regions and effectively utilize the superimposed regions, trimap generation is proposed for defining high, medium and low confidence regions from the multipath CNNs. The secondary path with low rank reconstructed input mitigates the conditions in which the primary segmentation network can potentially fail and overlook the boundary regions. The outcome of the segmentation is solved as an alpha matting problem by blending the trimap with the source input. RESULTS: Experiments on Osteoarthritis Initiative (OAI) datasets with all the 6 musculoskeletal tissues provide an overall segmentation dice score of 0.8925, where Femoral and Tibial part of cartilage achieving an average dice exceeding 0.9. The volumetric metrics also indicate the superior performances in all tissue compartments. CONCLUSIONS: Experiments on Osteoarthritis Initiative (OAI) datasets and a self-prepared scan validate the effectiveness of the proposed method. Inclusion of extra prediction scale allowed the model to distinguish and segment the tissue boundary accurately. We specifically demonstrate the application of the proposed method in a cartilage segmentation-based thickness map for diagnosis purposes.

Topological reorganization of the default mode network in patients with poststroke depressive symptoms: A resting-state fMRI study.

OBJECTIVE: This study mapped the topological configuration of the default mode network (DMN) in patients with depressive symptoms after acute ischemic stroke. METHODS: The study sample comprised 63 patients: 36 with poststroke depressive symptoms (PSD) and 37 without PSD matched according to age, gender and the severity of stroke. PSD was defined by a cutoff of ≥ 7 on the 15-item Geriatric Depression Scale (GDS). Resting-state functional magnetic resonance imaging (fMRI) was used to examine functional connectivity (FC) to reconstruct the DMN. Network based statistics estimated the FC differences of the DMN between the PSD and non-PSD groups. Graph theoretical approaches were used to characterize the topological properties of this network. RESULTS: The study sample mainly comprised patients with mild to moderate stroke. A widespread hyper-connected configuration of the functional DMN was characterized in PSD group. The orbital frontal, dorsolateral prefrontal, dorsal medial prefrontal and, ventromedial prefrontal corticis, the middle temporal gyrus and the inferior parietal lobule were the functional hubs related to PSD. The nodal topology in inferior parietal lobule and superior frontal gyrus, overlapping with dorsal medial prefrontal and, ventromedial prefrontal cortices, tended to be functionally integrated in patients with PSD. After False Discovery Rate correction, no significant difference between the PSD and non-PSD groups was found with respect to the global and nodal metrics of the DMN. However, the correlations between these altered network metrics and severity of PSD were lacking. LIMITATIONS: The diagnosis of PSD was based on the GDS score rather than established with a structured clinical interview. CONCLUSIONS: The DMN in PSD was functionally integrated and more specialized in some core hubs such as the inferior parietal lobule and dorsal prefrontal cortex. The configuration of the subnetwork like DMN may be more essential in the pathogenesis of PSD than single stroke lesions.

The associations of work style and physical exercise with the risk of work-related musculoskeletal disorders in nurses.

OBJECTIVES: This study aimed to investigate the prevalence of work-related musculoskeletal disorders (WMSD) in nurses, and to explore the association of work style and physical exercise with WMSD in this professional group. MATERIAL AND METHODS: In this study, a cross-sectional survey design was applied. A questionnaire survey was conducted on 692 nurses in 5 municipal hospitals, in the period August-October 2015. The survey included personal information, lifestyle, physical exercise and symptoms of WMSD. Chi2 test and logistic regression were used to identify the risk factors of WMSD. RESULTS: The prevalence of WMSD was 84% in all parts of the body in the previous year, with the highest prevalence in the neck (68.2%), followed by waist (67.6%) and shoulder (54.6%). Both univariate and multivariate analyses showed that physical exercise, night shifts and staying up late were associated with WMSD in nurses. The lack of exercise, night shifts and a tendency to stay up significantly increased the risk of WMSD (p < 0.05). CONCLUSIONS: The prevalence of musculoskeletal disorders among the municipal hospital nursing staff is high. An elevated risk of WMSD is observed in nurses who do not do exercises, work shifts and stay up late. Int J Occup Med Environ Health. 2019;32(1):15-24.

General self-efficacy modifies the effect of stress on burnout in nurses with different personality types.

BACKGROUND: Burnout is a health problem in nurses. Individuals with a certain personality are more susceptible to job-related burnout. General self-efficacy (GSE) is an important predictor of job-related burnout. The relationships between general self-efficacy, job-related burnout and different personality types are still not clear. This study aims to analyze the relationships of job-related burnout, stress, general self-efficacy and personality types, as well as their interactions in job-related burnout. METHOD: A cross-sectional survey of 860 nurses was conducted between June and July 2015 in China. We measured their job-related burnout using the scale of the Maslach Occupational Burnout Scale, and personality, stress, and GSE. Machine learning of generalized linear model were performed. RESULTS: Maslach Burnout Inventory (MBI) professional efficacy was significantly associated with gender, marital status, age, job title and length of service. A machine learning algorithm showed that stress was the most important factor in job-related burnout, followed by GSE, personality type (introvert unstable), and job title. Individuals with low GSE and either introversion or unstable (high neuroticism) personality seemed to have stronger burnout when they faced stress (regardless of stress intensity) compared to others. CONCLUSION: Stress, GSE and introvert unstable personality are the top three factors of job-related burnout. GSE moderates the effect of stress on burnout in nurses with extroversion or neuroticism personality. Reducing stress, increasing GSE, and more social support may alleviate job-related burnout in nurses. Nurses with introvert unstable personality should be given more social support in reducing stress and enhancing their GSE.

Job-related burnout is associated with brain neurotransmitter levels in Chinese medical workers: a cross-sectional study.

Objective The aim of the present study was to investigate the relationship between job burnout and neurotransmitter levels in medical staff. Methods A total of 80 medical staff were enrolled in the study and assessed for occupational burnout using the Maslach Burnout Inventory - General Survey (MBI-GS). The levels of neurotransmitters in the cerebral cortex were analysed using an SP03 encephalofluctuograph. Results The levels of the neurotransmitters γ-aminobutyric acid, 5-hydroxytryptamine (5-HT), norepinephrine (NE), glutamate, acetylcholine (Achl) and dopamine (DA) were significantly lower in men than in women. Medical staff with lower levels of exhaustion had significantly higher neurotransmitter levels than staff with moderate levels of exhaustion. However, there was no significant interaction between sex and exhaustion on neurotransmitter levels. Canonical correlation showed that exhaustion was positively associated with 5-HT and DA, but negatively associated with NE and Achl, regardless of age and sex. Conclusion Neurotransmitter levels in the cerebral cortex were associated with job-related burnout in medical staff. The findings suggest that long-term job-related burnout may lead to behavioural and psychiatric disorders.

2,3,7,8-Tetrachlorodibenzo-p-dioxin Mediated Cleft palate by Mouse Embryonic Palate Mesenchymal Cells.

OBJECTIVE: To evaluate the effects of mouse embryonic palatal mesenchymal (MEPM) cells by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) induced. STUDY DESIGN: An experimental study at Xinxiang Medical University. METHODS: Primary MEPM cells were derived from palatal tissue on 30 pregnant C57BL/6 female mice (embryonic day13, GD13). The MEPM cells were placed in a humidified incubator at 37°C with 5% CO2 atmosphere with media replaced every other day. The third passage cells were seeded, and one part of cells were treated with 10nM TCDD (TCDD group). And others were treated with DMSO (≤0.05%, as control group). After 72h, MTT assay was analysed cell viability. Scratch wound-healing was analysed cell motility. Flow cytometry was analysed cell apoptosis and cycle. Western blot was analysed the expression of cyclinE, CDK2, P16 and P21. RESULTS: TCDD inhibited the growth and migration of MEPM cells, while increased cell apoptosis. TCDD exposure inhibited the progression of cells from G1 to S phase and tended to reduce the number of cells entering the G2/M phase. TCDD inhibited expression of cyclinE and CDK2 at the protein level, instead increased the expression of P16 and p21 proteins. CONCLUSIONS: TCDD might induce cleft palate by altering MEPM cells.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

[Relationship of personality with job burnout and psychological stress risk in clinicians].

OBJECTIVE: To analyze the job burnout and mental health status of clinicians and the relationship of personality with job burnout and psychological stress, and to investigate the direct or indirect effects of personality on psychological stress. METHODS: Maslach Burnout Inventory-General Survey (MBI-GS), Eysenck Personality Questionnaire-Revised Short Scale (EPQ-RSC), and Kessler 10 Scale were administered to 775 clinicians. RESULTS: Of all clinicians, 29.5% had mild burnout, with a score of 22.7 ± 8.18 for psychological stress risk. The effect of personality on emotional exhaustion and cynicism was greater than that on personal accomplishment. Clinicians with a personality of introversion, neuroticism, and psychoticism suffered a higher risk of psychological stress. Personality had both direct and indirect effects on psychological stress. Neuroticism had the strongest effect on psychological stress, with an effect size of 0.55. CONCLUSION: Clinicians have a high level of both job burnout and mental psychological stress risk. Personality is significantly correlated with job burnout and psychological stress risk. Measures depending on personality should be taken for effective intervention.

General self-efficacy and the effect of hospital workplace violence on doctors' stress and job satisfaction in China.

OBJECTIVES: This study aims at exploring associations of general self-efficacy (GSE), workplace violence and doctors' work-related attitudes. MATERIAL AND METHODS: In this study a cross-sectional survey design was applied. Questionnaires were administrated to 758 doctors working in 9 hospitals of Zhengzhou, Henan province, China, between June and October 2010. General information on age, gender, and years of working was collected, and the doctors' experience and witnessing workplace violence, job satisfaction, job initiative, occupational stress as well as GSE were measured. General linear regression analysis was performed in association analyses. RESULTS: Both experiencing and witnessing workplace violence were significantly positively correlated with the level of occupational stress but significantly negatively correlated with job satisfaction, job initiative, and GSE. General self-efficacy significantly modified relationships between both experiencing and witnessing workplace violence with occupational stress (ß = 0.49 for experiencing violence; ß = 0.43 for witnessing violence; p < 0.001) and with job satisfaction (ß = -0.35 and -0.34, respectively; p < 0.05). However, it did not modify the relationships between both experiencing and witnessing workplace violence with job initiative (p > 0.05). The levels of occupational stress declined significantly with the increase of GSE, while job satisfaction increased significantly along with its increase. The effects of GSE on occupational stress and job satisfaction weakened as the frequency of violence increased. CONCLUSIONS: The findings suggest that GSE can modify effects of workplace violence on health care workers' stress and job satisfaction. Enhancing GSE in combination with stress reduction may lead to facilitating health care workers' recovery from workplace violence, and thereby improving their work-related attitudes.