Longitudinal proteomic investigation of COVID-19 vaccination (preprint)

Although the development of COVID-19 vaccines has been a remarkable success, the heterogeneous individual antibody generation and decline over time are unknown and still hard to predict. In this study, blood samples were collected from 163 participants who next received two doses of an inactivated COVID-19 vaccine (CoronaVac) at a 28-day interval. Using TMT-based proteomics, we identified 1715 serum and 7342 peripheral blood mononuclear cells (PBMCs) proteins. We proposed two sets of potential biomarkers (seven from serum, five from PBMCs) using machine learning, and predicted the individual seropositivity 57 days after vaccination (AUC = 0.87). Based on the four PBMC's potential biomarkers, we predicted the antibody persistence until 180 days after vaccination (AUC = 0.79). Our data highlighted characteristic hematological host responses, including altered lymphocyte migration regulation, neutrophil degranulation, and humoral immune response. This study proposed potential blood-derived protein biomarkers for predicting heterogeneous antibody generation and decline after COVID-19 vaccination, shedding light on immunization mechanisms and individual booster shot planning.

Coronavirus disease 2019 in pregnancy

Objectives: This study aimed to compare clinical courses and outcomes between pregnant and reproductive-aged non-pregnant women with COVID-19, and to assess the vertical transmission potential of COVID-19 in pregnancy.

Psychological symptom and its influencing factors among Chinese elementary school students during early COVID-19 outbreak: A cross-section survey (preprint)

Background: This study aimed to examine the psychological symptom and its influencing factors among Chinese elementary school students during early COVID-19 outbreak. Methods A cross-section survey was used to investigate individual psychological response and its influencing factors among Chinese elementary school students. And the data were analyzed by the statistical software of SPSS 16.0. Results The results showed that students who experience large number of COVID-19-related stressors tend to report more depression and anxiety symptoms. Both problem-based coping and online learning satisfaction were protectors in predicting students’ symptoms of depression and anxiety, while emotion-based coping was a vulnerable factor. With regard to demographic variables, students with low family social status reported more symptoms of depression and anxiety than those with high family social status. Conclusions These findings present implications for psychological inventions and for education counselling services during the pandemics.