Burnout, depression, anxiety and insomnia among medical staff during the COVID-19 epidemic in Shanghai.

Background: Coronavirus disease 2019 (COVID-19) has progressively impacted our daily lives, resulting in unexpected physical and mental stress on medical staff. This study is designed to investigate the levels of and risk factors for burnout, depression, anxiety, and insomnia among medical staff during the COVID-19 epidemic breakout in Shanghai, China. Methods: This cross-sectional survey was conducted from May 1 to May 31, 2022, among medical staff who were on the frontline during the epidemic breakout in Shanghai from different institutions. The MBI-HSS was used to assess burnout, PHQ-9, GAD-7 and ISI were used to evaluate mental status and insomnia. Results: A total of 543 valid questionnaires were collected. The depersonalization, depression, anxiety, and insomnia scores of medical staff were significantly higher during the pandemic in Shanghai compared with norms, while lack of personal achievement scores were decreased. Working time, work unit, work environment and age are important influencers of burnout, depression and anxiety of medical staff. Long working hours are the most likely causes of burnout and emotional disorders. Medical staff in primary hospitals were most likely to suffer from burnout and emotional disorders, while medical staff in tertiary hospitals had a reduced sense of personal achievement. Young medical staff are prone to negative emotions such as depression and anxiety, while older medical staff have a lower sense of personal accomplishment. Medical staff who were not in the shelter hospitals or designated hospitals were more likely to have problems of emotional exhaustion, depersonalization and anxiety than those who were in the shelter hospitals or designated hospitals. Contracting COVID-19 had no effect on medical staff. Emotional exhaustion and depersonalization were positively correlated with anxiety, depression, and sleep disorders while personal achievement was negatively correlated with these factors. Conclusion: Medical staff in Shanghai had high burnout, depression, anxiety and insomnia levels during the epidemic outbreak in Shanghai. During the COVID-19, medical staff may suffer different psychological problems which should be concerned. Care and supports about burnout, mental health and insomnia need to be taken to promote the mental health of medical staff according to different characteristics of medical staff.

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection.

Background: A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection. Methods: The surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information. Results: A prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5. Conclusions: In a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.