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Dynamic changes of the paired heavy and light chain B cell receptor (BCR) repertoire provide an essential insight into understanding the humoral immune response post-SARS-CoV-2 infection and vaccination. However, differences between the endogenous paired BCR repertoire kinetics in SARS-CoV-2 infection and previously recovered/naïve subjects treated with the inactivated vaccine remain largely unknown. We performed single-cell V(D)J sequencing of B cells from six healthy donors with three shots of inactivated SARS-CoV-2 vaccine (BBIBP-CorV), five people who received the BBIBP-CorV vaccine after having recovered from COVID-19, five unvaccinated COVID-19 recovered patients and then integrated with public data of B cells from four SARS-CoV-2-infected subjects. We discovered that BCR variable (V) genes were more prominently used in the SARS-CoV-2 exposed groups (both in the group with active infection and in the group that had recovered) than in the vaccinated groups. The VH gene that expanded the most after SARS-CoV-2 infection was IGHV3-33, while IGHV3-23 in the vaccinated groups. SARS-CoV-2-infected group enhanced more BCR clonal expansion and somatic hypermutation than the vaccinated healthy group. A small proportion of public clonotypes were shared between the SARS-CoV-2 infected, vaccinated healthy, and recovered groups. Moreover, several public antibodies had been identified against SARS-CoV-2 spike protein. We comprehensively characterize the paired heavy and light chain BCR repertoire from SARS-CoV-2 infection to vaccination, providing further guidance for the development of the next-generation precision vaccine.

Background As a highly infectious disease with human-to-human transmission characteristics, COVID-19 has caused panic in the general public. Those who have recovered from COVID-19 may experience discrimination and internalized stigma. They may be more likely to worry about social interaction and develop social anxiety. Objectives This study investigated the associations among hospitalization factors, social/interpersonal factors, personal factors, and social anxiety to reveal the mechanism of social anxiety in COVID-19 survivors. Methods A cross-sectional, multicenter telephone survey was conducted from July to September 2020 in five Chinese cities (i.e. Wuhan, Nanning, Shenzhen, Zhuhai, and Dongguan); adult COVID-19 survivors were recruited 6 months after they were discharged from the hospital. Linear regressions and path analysis based on the minority stress model were conducted to test the relationships among hospitalization, social/interpersonal factors, personal factors, and social anxiety. Results The response rate was 74.5% (N = 199, 55.3% females). Linear regression analyses showed that various hospitalization, social/interpersonal, and personal factors were statistically significantly associated with social anxiety. Path analysis showed that the proposed model fit the data well (χ2(df) = 3.196(3), p = .362, CFI = .999, NNFI = .996, RMSEA = .018). Internalized stigma fully mediated the association between perceived discrimination/social support and social anxiety, while it partially mediated the association between perceived affiliate stigma and social anxiety. Conclusions The results suggest that social/interpersonal and personal factors have a stronger association with social anxiety than hospitalization factors and highlight the importance of internalized stigma in understanding the mechanisms of these relationships. Clinical psychologists can refer to these modifiable psychosocial factors to develop efficient interventions for mental health promotion., HIGHLIGHTS Internalized stigma fully mediated the effects of perceived discrimination and social support on social anxiety and partially mediated the effect of perceived affiliate stigma on social anxiety.

Background Many COVID-19 survivors reported stigmatization after recovery. This study investigated the association between stigma (discrimination experiences, self-stigma and perceived affiliate stigma) and sleep quality among COVID-19 survivors six months after hospital discharge. Methods Participants were recovered adult COVID-19 survivors discharged between February 1 and April 30, 2020. Medical staff of five participating hospitals approached all discharged COVID-19 period during this period. A total of 199 participants completed the telephone interview during July to September, 2020. Structural equation modeling was performed to test the hypothesize that resilience and social support would mediate the associations between stigma and sleep quality. Results The results showed that 10.1% of the participants reported terrible/poor sleep quality, 26.1% reported worse sleep quality in the past week when comparing their current status versus the time before COVID-19. After adjusting for significant background characteristics, participants who had higher number of discrimination experience, perceived stronger self-stigma and stronger perceived affiliate stigma reported poorer sleep quality. Resilience and social support were positively and significantly associated with sleep quality. The indirect effect of self-stigma on sleep quality through social support and resilience was significant and negative. Perceived affiliate stigma also had a significant and negative indirect effect on sleep quality through social support and resilience. Conclusions Various types of stigma after recovery were associated with poor sleep quality among COVID-19 survivors, while social support and resilience were protective factors. Resilience and social support mediated the associations between self-stigma/perceived affiliate stigma and sleep quality.

ObjectiveTo analyze the epidemiological history, clinical symptoms, laboratory testing parameters of patients with mild and severe COVID-19 infection, and provide a reference for timely judgment of changes in the patients’ conditions and the formulation of epidemic prevention and control strategies.MethodsA retrospective study was conducted in this research, a total of 90 patients with COVID-19 infection who received treatment from January 21 to March 31, 2020 in the Ninth People’s Hospital of Dongguan City were selected as study subject. We analyzed the clinical characteristics of laboratory-confirmed patients with COVID-19, used the oversampling method (SMOTE) to solve the imbalance of categories, and established Lasso-logistic regression and random forest models.ResultsAmong the 90 confirmed COVID-19 cases, 79 were mild and 11 were severe. The average age of the patients was 36.1 years old, including 49 males and 41 females. The average age of severe patients is significantly older than that of mild patients (53.2 years old vs 33.7 years old). The average time from illness onset to hospital admission was 4.1 days and the average actual hospital stay was 18.7 days, both of these time actors were longer for severe patients than for mild patients. Forty-eight of the 90 patients (53.3%) had family cluster infections, which was similar among mild and severe patients. Comorbidities of underlying diseases were more common in severe patients, including hypertension, diabetes and other diseases. The most common symptom was cough [45 (50%)], followed by fever [43 (47.8%)], headache [7 (7.8%)], vomiting [3 (3.3%)], diarrhea [3 (3.3%)], and dyspnea [1 (1.1%)]. The laboratory findings of patients also included leukopenia [13(14.4%)] and lymphopenia (17.8%). Severe patients had a low level of creatine kinase (median 40.9) and a high level of D-dimer. The median NLR of severe patients was 2.82, which was higher than that of mild patients. Logistic regression showed that age, phosphocreatine kinase, procalcitonin, the lymphocyte count of the patient on admission, cough, fatigue, and pharynx dryness were independent predictors of COVID-19 severity. The classification of random forest was predicted and the importance of each variable was displayed. The variable importance of random forest indicates that age, D-dimer, NLR (neutrophil to lymphocyte ratio) and other top-ranked variables are risk factors.ConclusionThe clinical symptoms of COVID-19 patients are non-specific and complicated. Age and the time from onset to admission are important factors that determine the severity of the patient’s condition. Patients with mild illness should be closely monitored to identify those who may become severe. Variables such as age and creatine phosphate kinase selected by logistic regression can be used as important indicators to assess the disease severity of COVID-19 patients. The importance of variables in the random forest further complements the variable feature information.

B cell response plays a critical role against SARS-CoV-2 infection. However, little is known about the diversity and frequency of the paired SARS-CoV-2 antigen-specific BCR repertoire after SARS-CoV-2 infection. Here, we performed single-cell RNA sequencing and VDJ sequencing using the memory and plasma B cells isolated from five convalescent COVID-19 patients, and analyzed the spectrum and transcriptional heterogeneity of antibody immune responses. Via linking BCR to antigen specificity through sequencing (LIBRA-seq), we identified a distinct activated memory B cell subgroup (CD11chighCD95high) had a higher proportion of SARS-CoV-2 antigen-labeled cells compared with memory B cells. Our results revealed the diversity of paired BCR repertoire and the non-stochastic pairing of SARS-CoV-2 antigen-specific immunoglobulin heavy and light chains after SARS-CoV-2 infection. The public antibody clonotypes were shared by distinct convalescent individuals. Moreover, several antibodies isolated by LIBRA-seq showed high binding affinity against SARS-CoV-2 receptor-binding domain (RBD) or nucleoprotein (NP) via ELISA assay. Two RBD-reactive antibodies C14646P3S and C2767P3S isolated by LIBRA-seq exhibited high neutralizing activities against both pseudotyped and authentic SARS-CoV-2 viruses in vitro. Our study provides fundamental insights into B cell response following SARS-CoV-2 infection at the single-cell level.