Background: Due to the novel coronavirus epidemic, medical workers are under immense psychological pressure. As such, the East Campus of Shanghai Sixth People's Hospital actively adopted the Symptoms Checklist 90 (SCL-90) to evaluate the mental health of hospital staff before and after the psychological intervention from the Employee Assistance Program (EAP).Methods: Medical workers from the East Campus of Shanghai Sixth People's Hospital were recruited for this study. Psychological evaluations were conducted using the SCL-90, with a score of >160 regarded as a positive result, or in other words, an indication of abnormal psychological symptoms. The EAP adopted different forms of psychological interventions for healthcare professionals, and participation in these measures was entirely voluntary. Medical workers completed the SCL-90 again after participating in the psychological intervention, and we analyzed the changes between their two assessments.Results: Of the 1,198 total medical staff present at the hospital, 844 participated in the initial survey, while only 652 completed the survey a second time (i.e., post-psychological intervention). Multivariate logistic regression analysis found that the psychological status of hospital staff was correlated with gender, education background, and fertility status (P < 0.05). The results showed that, compared with women, men's mental health status was better, with an OR value of 0.598 (0.372–0.962). Groups with high school, junior high school, and below education levels were at higher risk of psychological problems, with OR values of 23.655 (2.815–198.784) and 9.09 (2.601–31.801), respectively. Administrative occupations and having two or more children were protective factors for mental health, and the OR values were 0.400 (0.175–0.912) and 0.327 (0.152–0.703), respectively.Following the psychological intervention, we found that the mental health of hospital workers improved, as indicated by their second SCL-90 evaluations, although the proportion of medical staff willing to participate in the second evaluation was lower than the initial assessment. There were differences in the SCL-90 scores among different occupations, and there were also differences in the scores of employees of different occupations who had participated in the two evaluations. The employees of different positions who participated in the two evaluations were matched and analyzed and found that the depression and anxiety of the doctor group were significantly reduced. In the nursing group, the total score, somatization, interpersonal sensitivity, depression, and anxiety were significantly reduced. In the medical technician group, depression, anxiety, and paranoia were reduced considerably. Among office staff, no significant differences were found. Among workers, the total score, depression, and anxiety were significantly reduced.Conclusion: Hospitals have the potential to alleviate and reduce the psychological pressure placed on medical staff members through the EAP, which can actively adopt intervention and guidance measures. The findings of this study have important implications, as reducing abnormal psychological symptoms of healthcare professionals can be helpful in the fight against the coronavirus epidemic.

IntroductionInfant jaundice is a common condition which results from a high concentration of serum bilirubin. Phototherapy is a widely-used treatment for bilirubin clearance.We analyzed the effect of phototherapy on intestinal flora and metabolism of newborns. The aim was to assess the benefit of treatment for hyperbilirubinemia with phototherapy.Material and methodsFifty-three jaundiced infants hospitalized at our neonatal intensive care unit were treated with phototherapy. Of them, 29 were prescribed antibiotics during the hospitalization. Fecal samples were collected before and 24 h and 48 h after phototherapy. The bacterial species and relative abundance were identified with Macrogene sequencing. The bile acids in feces were identified using liquid chromatography-mass spectroscopy (LC-MS).ResultsDifferential microbial species/genera and secondary bile acids were found after phototherapy. There are significant differences in the changes of the microbial species/genera between infants who did not receive antibiotic treatment and those who were given antibiotic treatment. Secondary bile acids were also significantly altered. At the same time, the differential microbial species/genera and the differential secondary bile acids interacted with each other.ConclusionsThis study identified several differential intestinal microbial species and secondary bile acids in fecal samples from infants with jaundice before and after phototherapy.Phototherapy can change the flora and its metabolism and Its long-term impact needs further observation.

BackgroundStudies show that lung ultrasound (LUS) can accurately diagnose community-acquired pneumonia (CAP) and keep children away from radiation, however, it takes a long time and requires experienced doctors. Therefore, a robust, automatic and computer-based diagnosis of LUS is essential.ObjectiveTo construct and analyze convolutional neural networks (CNNs) based on transfer learning (TL) to explore the feasibility of ultrasound image diagnosis and grading in CAP of children.Methods89 children expected to receive a diagnosis of CAP were prospectively enrolled. Clinical data were collected, a LUS images database was established comprising 916 LUS images, and the diagnostic values of LUS in CAP were analyzed. We employed pre-trained models (AlexNet, VGG 16, VGG 19, Inception v3, ResNet 18, ResNet 50, DenseNet 121 and DenseNet 201) to perform CAP diagnosis and grading on the LUS database and evaluated the performance of each model.ResultsAmong the 89 children, 24 were in the non-CAP group, and 65 were finally diagnosed with CAP, including 44 in the mild group and 21 in the severe group. LUS was highly consistent with clinical diagnosis, CXR and chest CT (kappa values = 0.943, 0.837, 0.835). Experimental results revealed that, after k-fold cross-validation, Inception v3 obtained the best diagnosis accuracy, PPV, sensitivity and AUC of 0.87 ± 0.02, 0.90 ± 0.03, 0.92 ± 0.04 and 0.82 ± 0.04, respectively, for our dataset out of all pre-trained models. As a result, best accuracy, PPV and specificity of 0.75 ± 0.03, 0.89 ± 0.05 and 0.80 ± 0.10 were achieved for severity classification in Inception v3.ConclusionsLUS is a reliable method for diagnosing CAP in children. Experiments showed that, after transfer learning, the CNN models successfully diagnosed and classified LUS of CAP in children; of these, the Inception v3 achieves the best performance and may serve as a tool for the further research and development of AI automatic diagnosis LUS system in clinical applications.Registrationwww.chictr.org.cn ChiCTR2200057328.