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Guo Li,1 Ping Jing,2 Guohua Chen,3 Junhua Mei,3 Jinfeng Miao,1 Wenzhe Sun,1 Yan Lan,1 Xin Zhao,1 Xiuli Qiu,1 Zhou Zhu,1,* Suiqiang Zhu1,* 1Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People’s Republic of China; 2Department of Neurology, Wuhan Central Hospital, Wuhan, Hubei, 430014, People’s Republic of China; 3Department of Neurology, Wuhan First Hospital, Wuhan, Hubei, 430022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhou Zhu Tel +86-18171081029Email zhouzhu@hust.edu.cnSuiqiang Zhu Tel +86-13035101141Email zhusuiqiang@163.comPurpose: The early detection of major post-stroke depression (PSD) is essential to optimize patient care. A major PSD prediction tool needs to be developed and validated for early screening of major PSD patients.Patients and Methods: A total of 639 acute ischemic stroke (AIS) patients from three hospitals were consecutively recruited and completed a 3-month follow-up. Sociodemographic, clinical and laboratory test data were collected on admission. With major depression criteria being met in the DSM-V, 17-item Hamilton Rating Scale For Depression (HRSD) score ≥ 17 at 3 months after stroke onset was regarded as the primary endpoint. Multiple imputation was used to substitute the missing values and multivariable logistic regression model was fitted to determine associated factors with a bootstrap backward selection process. The nomogram was constructed based on the regression coefficients of the associated factors. Performance of the nomogram was assessed by discrimination (C-statistics) and calibration curve.Results: A total of 7.04% (45/639) of patients were diagnosed with major PSD at 3 months. The final logistic regression model included age, baseline NIHSS and mRS scores, educational level, calcium–phosphorus product, history of hypertension and atrial fibrillation. The model had acceptable discrimination, based on a C-statistic of 0.81 (95% CI, 0.791– 0.829), with 71.1% sensitivity and 78.6% specificity. We also transformed the model to a nomogram, an easy-to-use clinical tool which could be used to facilitate the early screening of major PSD patients at 3 months.Conclusion: We identified several associated factors of major PSD at 3 months and constructed a convenient nomogram to guide follow-up and aid accurate prognostic assessment.Keywords: major post-stroke depression, nomogram, calcium–phosphorus product, C-statistic

Background: The coronavirus disease-2019 (COVID-19) outbreak and a 3-month lockdown of Wuhan may have had a long-term impact on the mental health of frontline healthcare workers (HWs). However, there is still a lack of comparative studies on the mental health of front-line HWs in the initial phase of the lockdown and 1 month after the lifting of the lockdown.Methods: We recruited 1717 HWs during the initial phase of the lockdown and 2214 HWs 1 month after the lifting of the lockdown, and their baseline characteristics and psychiatric health in these two phases were compared. Furthermore, Pearson's Chi-square test and multivariate logistic regression analysis were used to determine the possible risk factors associated with depressive symptoms in the front-line HWs.Results: Compared with the initial phase of the lockdown, the proportion of HWs with anxiety symptoms and stress decreased, while the proportion of HWs with depressive symptoms increased a month after the lifting of the lockdown. Male sex, exercise habit, comorbidities, and having family members or relatives with suspected or confirmed COVID-19 infection were significantly related to the increased incidence of depressive symptoms during the initial phase of the lockdown. Comorbidities, negative effect of media coverage, working >4 days a week, lower annual household income, and deteriorating relationships with family members were associated with depressive symptoms a month after the lifting of the lockdown.Conclusion: The increased proportion of HWs with depressive symptoms 1 month after the lifting of the lockdown suggested that mental health of front-line HWs should be a top-priority issue, not only during, but also after the pandemic.

Background Exploring etiological clues to adolescent depression, especially in female adolescents, might be helpful to improve the social environment of female adolescents. The aim at this study is to explore psycho-social factors of female adolescents with high depressive symptomatology and gender differences in depressive symptoms among Chinese adolescents. Method We examined 4100 adolescents from Wuhan city and Jianli county via a cross-sectional study. Depressive symptomatology was screened through the Chinese version of Center for Epidemiology Studies Depression Scale. Multivariate logistic regression was performed to explore the factors related to high depressive symptomatology in female and male adolescents, respectively. Results The prevalence of high depressive symptomatology in female and male were 38.9 and 30.2% respectively. The psycho-social factors of high depressive symptomatology in female adolescents were age (Adjusted odds ratio [aOR] = 1.201, 95% confidence interval [CI], 1.076 ~ 1.341), single parent family (aOR = 2.004, 95%CI, 1.448 ~ 2.772) and fathers’ education level (compared to primary school and below, [Junior middle school, aOR = 0.641, 95%CI, 0.439 ~ 0.934; Senior middle school, aOR = 0.603, 95%CI, 0.410 ~ 0.888; College degree and above, aOR = 0.639, 95%CI, 0.437 ~ 0.936]). Conclusion Fathers’ education level was associated with high depressive symptomatology in female adolescents. Female adolescents whose father with primary school education or below deserves more attention. Further epidemiologic researches need to be conducted to explore the different risk factors between female and male adolescents in China.

BACKGROUND: Providing high-quality training to residency students during the coronavirus 2019 (COVID-19) pandemic has been a goal of our institution. Since 2108, we began to take microlectures to students teaching. Microlectures are online presentations, and the microlecture teaching method has many advantages, such as a short teaching time, situational resource composition, diverse communication, strong pertinence and can attend microlectures from home. The aim of the present study was to evaluate the advantages of the microlecture teaching method on students in standardized residency training. METHODS: Students from our department were randomly divided into the traditional teaching group (control group) and the microlecture teaching group (observation group). The teaching duration for both groups was 3 months. All students were assessed on basic knowledge of the neurology before enrollment. After the teaching session, the students were assessed on teaching effect, theoretical operation, and clinical practice satisfaction. The students also evaluated the teachers, and the teachers evaluated the students. RESULTS: A total of 84 students participated in the study and were divided equally into the observation group (42 students) and the control group (42 students). The results showed that the rate of reaching the standard of teaching effect, achievement of theory and operation, satisfaction with clinical practice, the student's grades by teachers, and student satisfaction with teachers were significantly higher in the observation group than in the control group (all P

The segmentation of the neuronal cellular images has important clinical use as it helps obtain essential information on the nervous system for diagnosing and curing diseases. Traditional automotive segmentation methods including watershed method have problems like being sensitive to the noise and depending on the images' uniformity. These years, neural networks based on the U-net structure have shown their good performance especially for the medical image segmentation. They are end-to-end, with high precision and efficiency, able to extract deep features inside the images. In this work, a U-net neural network is used for neuronal structure segmentation in Drosophila first instar larva ventral nerve cord. Only ten images are used for training, and the prediction results for another ten testing images give out network's performance as a ROC's AUC at 0.9539 and a PR's AUC at 0.9870. Confusion matrix shows 95% of the '1' pixels and 75% of the '0' pixels could be predicted correctly, which is a rather ideal result for the network.