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Objectives: At present, the doctor-patient relationship is tense. The prevalence of negative emotions, such as depression and anxiety, among healthcare workers is increasing every year. Negative attitudes of medical workers toward mental problems may aggravate the doctor-patient conflict and psychological problems of medical workers. This study aims to explore the complex network relationships between outpatient medical workers' attitudes toward mental problems, doctor-patient relationships, and their depression/anxiety levels., Methods: A total of 578 outpatient medical staff from the Second Xiangya Hospital of Central South University (167 males, 411 females) completed questionnaires on their attitudes toward mental problems, doctor-patient relationships, and depression/anxiety symptoms. Network analysis was conducted separately to construct the "attitude towards mental problems-doctor-patient relationship network" and "depression-anxiety related network"., Results: The edge between "M15 (insulting words)" and "D8 (waste time)" showed the strongest strength in the "attitude towards mental problems-doctor-patient relationship network", and "M15 (insulting words)" had the highest bridge strength in the network. For the analysis of emotional variables, "P1 (anhedonia)" showed the most obvious association with "D10 (communication difficulties)" in the doctor-patient relationship and "M2 (poor quality of life)" in the psychiatric attitudes, and "P1 (anhedonia)" was the key bridge symptom in the network., Conclusions: The "insulting words" may be an intervention target for medical workers' attitudes toward mental problems. The "anhedonia" in depression is the potential symptom that needs to be treated. Intervention targeting these variables may be beneficial to improve the mental health level of medical workers and the doctor-patient relationship.

Objectives: Firefighters are prone to suffer from psychological trauma and post-traumatic stress disorder (PTSD) in the workplace, and have a poor prognosis after PTSD. Reliable models for predicting PTSD allow for effective identification and intervention for patients with early PTSD. By collecting the psychological traits, psychological states and work situations of firefighters, this study aims to develop a machine learning algorithm with the aim of effectively and accurately identifying the onset of PTSD in firefighters, as well as detecting some important predictors of PTSD onset., Methods: This study conducted a cross-sectional survey through convenient sampling of firefighters from 20 fire brigades in Changsha, which were evenly distributed across 6 districts and Changsha County, with a total of 628 firefighters. We used the synthetic minority oversampling technique (SMOTE) to process data sets and used grid search to finish the parameter tuning. The predictive capability of several commonly used machine learning models was compared by 5-fold cross-validation and using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, precision, recall, and F1 score., Results: The random forest model achieved good performance in predicting PTSD with an average AUC score at 0.790. The mean accuracy of the model was 90.1%, with an F1 score of 0.945. The three most important predictors were perseverance, forced thinking, and reflective deep thinking, with weights of 0.165, 0.158, and 0.152, respectively. The next most important predictors were employment time, psychological power, and optimism., Conclusions: PTSD onset prediction model for Changsha firefighters constructed by random forest has strong predictive ability, and both psychological characteristics and work situation can be used as predictors of PTSD onset risk for firefighters. In the next step of the study, validation using other large datasets is needed to ensure that the predictive models can be used in clinical setting.

Objectives: Major depressive disorder (MDD) patients with anhedonia tend to have a poor prognosis. The underlying imaging basis for anhedonia in MDD remains largely unknown. The relationship between nodal properties and anhedonia in MDD patients need to be further investigated. Herein, this study aims to explore differences of cerebral functional node characteristics in MDD patients with severe anhedonia (MDD-SA) and MDD patients with mild anhedonia (MDD-MA) before and after the antidepressant treatment., Methods: Ninety participants with current MDD were recruited in this study. 24-Item Hamilton Depression Scale (HAMD-24) and Snaith-Hamilton Pleasure Scale (SHAPS) were used to assess the severity of depression and anhedonia at baseline and the end of 6-months treatment. The MDD patients who scored above the 25th percentile on the SHAPS were assigned to an MDD-SA group ( n= 19), while those who scored below the 25th percentile were assigned to an MDD-MA group ( n= 18). All patients in the 2 groups received antidepressant treatment. Functional magnetic resonance imaging (fMRI) images of all the patients were collected at baseline and the end of 6-months treatment. Graph theory was applied to analyze the patients' cerebral functional nodal characteristics, which were measured by efficiency (e i ) and degree (k i )., Results: Repeated measures 2-factor ANCOVA showed significant main effects on group on the e i and k i values of left superior frontal gyrus (LSFG) ( P= 0.003 and P= 0.008, respectively), and on the e i and k i values of left medial orbital-frontal gyrus (LMOFG) ( P =0.004 and P= 0.008, respectively). Compared with the MDD-MA group, the significantly higher e i and k i values of the LSFG ( P= 0.015 and P= 0.021, respectively), and the significantly higher e i and k i values of the LMOFG ( P =0.015 and P= 0.037, respectively) were observed in the MDD-SA group at baseline. Meanwhile, higher SHAPS scores could result in higher e i and k i values of LSFG ( P =0.019 and P= 0.026, respectively), and higher e i value of LMOFG ( P= 0.040) at baseline; higher SHAPS scores could result in higher e i values of LSFG ( P= 0.049) at the end of 6-months treatment. The multiple linear regression analysis revealed that sex were negatively correlated with the e i and k i values of LSFG ( r = -0.014, P =0.004; r =-1.153, P =0.001, respectively). The onset age of MDD was negatively correlated with the k i value of LSFG ( r =-0.420, P =0.034) at the end of 6-months treatment. We also found that SHAPS scores at baseline were positively correlated with the HAMD-24 scores ( r =0.387, P =0.022) at the end of 6-months treatment., Conclusions: There are obvious differences in nodal properties between the MDD-SA and the MDD-MA patients, such as the high e i of LSFG in the MDD-SA patients, which may be associated with the severity of anhedonia. These nodal properties could be potential biomarkers for the prognosis of MDD. The increased e i and k i values in the LSFG of MDD-SA patients may underlie a compensatory mechanism or protective mechanism. The mechanism may be an important component of the pathological mechanism of MDD-SA. The poor prognosis in the MDD-SA patients suggests that anhedonia may predict a worse prognosis in MDD patients. Sex and onset age of MDD may affect the nodal properties of LSFG at baseline and the end of 6-months treatment.