Objective To investigate the influence of social support on the depression and anxiety of standardized training residents.Methods Three hundred and eighteen standardized training residents selected by random sampling method from 4 training bases in Shanghai participated in this survey.ResultsThe average score of PHQ-9 was(7.24±5.20), and 67.30%(n=214)of participants had different degrees of depression;the average score of GAD-7 was(5.57±4.55), and 55.03%(n=175)of participants had different degrees of anxiety.One-way analysis of variances showed that standardized training residents who had work experience, longer training years and less salary satisfaction got higher scores in PHQ-9 and GAD-7.Male physicians had higher scores in GAD-7 than female ones.The scores of PHQ-9 (r=-0.390, P<0.01) and GAD-7 (r=-0.376, P<0.01) were both negatively correlated with social support.Regression analysis showed that training years,salary satisfaction,objective social support and the availability of support were significant for predicting the scores of PHQ-9(adjusted R2=0.242,F=17.893), work experience, salary satisfaction,objective social support and the availability of support were significant for predicting the scores of GAD-7(adjusted R2=0.228,F=14.390).After controlled the demographic variables, social support explained the variation rate of 0.119 to the score of PHQ-9 and 0.126 to the score of GAD-7.Conclusion The depression and anxiety of standardized training residents in this study is in a serious situation.Providing the objective social support and the availability of support as well as improving the salary satisfaction of standardized training residents may relieve the depression and anxiety and enhance their mental health.

Objective This study is to explore the association between self-differentiation and men-tal health among medical students. Methods Differentiation of self inventory-revised (DSI-R) and univer-sity personality inventory (UPI) were used to measure the self-differentiation and mental health of 526 med-ical students from Grade One to Grade Five at a comprehensive university in Shanghai. Result The mean score of DSI-R was (171.25±19.65). 32.2％ of participants had different levels of mental health prob-lems. Female students got higher score of DSI-R than male students (P=0.007). Statistically significant dif-ferences of medical students' self-differentiation were found among years of school attended (P=0.039). Sta-tistically significant differences of self-differentiation were also found between the first class and the third class of UPI (P<0.001) as well as the second class and the third class of UPI (P=0.004). Ordinal regression analysis indicated that self-differentiation was a risk factor for medical students' mental health (OR=1.036, P=0.000). Conclusion The average score of medical students' self-differentiation was higher than college students of other specialties. But their mental health condition was worse. Medical students with higher self-differentia-tion had worse mental health situation.

Early distinction of bipolar disorder (BD) from major depressive disorder (MDD) is difficult since no tools are available to estimate the risk of BD. In this study, we aimed to develop and validate a model of oxidative stress injury for predicting BD. Data were collected from 1252 BD and 1359 MDD patients, including 64 MDD patients identified as converting to BD from 2009 through 2018. 30 variables from a randomly-selected subsample of 1827 (70%) patients were used to develop the model, including age, sex, oxidative stress markers (uric acid, bilirubin, albumin, and prealbumin), sex hormones, cytokines, thyroid and liver function, and glycolipid metabolism. Univariate analyses and the Least Absolute Shrinkage and Selection Operator were applied for data dimension reduction and variable selection. Multivariable logistic regression was used to construct a model for predicting bipolar disorder by oxidative stress biomarkers (BIOS) on a nomogram. Internal validation was assessed in the remaining 784 patients (30%), and independent external validation was done with data from 3797 matched patients from five other hospitals in China. 10 predictors, mainly oxidative stress markers, were shown on the nomogram. The BIOS model showed good discrimination in the training sample, with an AUC of 75.1% (95% CI: 72.9%-77.3%), sensitivity of 0.66, and specificity of 0.73. The discrimination was good both in internal validation (AUC 72.1%, 68.6%-75.6%) and external validation (AUC 65.7%, 63.9%-67.5%). In this study, we developed a nomogram centered on oxidative stress injury, which could help in the individualized prediction of BD. For better real-world practice, a set of measurements, especially on oxidative stress markers, should be emphasized using big data in psychiatry.
Biomarkers/metabolism* ; Bipolar Disorder/metabolism* ; Depressive Disorder, Major/diagnosis* ; Early Diagnosis ; Humans ; Oxidative Stress