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BackgroundPre-existing cross-reactive immunity among different coronaviruses, also termed immune imprinting, may have a comprehensive impact on subsequent SARS-CoV-2 infection and COVID-19 vaccination effectiveness. Here, we aim to explore the interplay between pre-existing seasonal coronaviruses (sCoVs) antibodies and the humoral immunity induced by COVID-19 vaccination.MethodsWe first collected serum samples from healthy donors prior to COVID-19 pandemic and individuals who had received COVID-19 vaccination post-pandemic in China, and the levels of IgG antibodies against sCoVs and SARS-CoV-2 were detected by ELISA. Wilcoxon rank sum test and chi-square test were used to compare the difference in magnitude and seropositivity rate between two groups. Then, we recruited a longitudinal cohort to collect serum samples before and after COVID-19 vaccination. The levels of IgG antibodies against SARS-CoV-2 S, S1, S2 and N antigen were monitored. Association between pre-existing sCoVs antibody and COVID-19 vaccination-induced antibodies were analyzed by Spearman rank correlation.Results96.0% samples (339/353) showed the presence of IgG antibodies against at least one subtype of sCoVs. 229E and OC43 exhibited the highest seroprevalence rates at 78.5% and 72.0%, respectively, followed by NL63 (60.9%) and HKU1 (52.4%). The levels of IgG antibodies against two β coronaviruses (OC43 and HKU1) were significantly higher in these donors who had inoculated with COVID-19 vaccines compared to pre-pandemic healthy donors. However, we found that COVID-19 vaccine-induced antibody levels were not significant different between two groups with high levelor low level of pre-existing sCoVs antibody among the longitudinal cohort.ConclusionWe found a high prevalence of antibodies against sCoVs in Chinese population. The immune imprinting by sCoVs could be reactivated by COVID-19 vaccination, but it did not appear to be a major factor affecting the immunogenicity of COVID-19 vaccine. These findings will provide insights into understanding the impact of immune imprinting on subsequent multiple shots of COVID-19 vaccines.

COVID-19 survivors who had acute respiratory symptoms might experience prolonged post-traumatic stress disorder (PTSD) due to further rehabilitation, somatic symptoms and related distress. The conservation of resource (COR) theory is a well-developed theory to understand how people develop PTSD symptoms in traumatic events. The current study aimed to examine the potential factors of PTSD symptoms and interrelationships among this factors among COVID-19 survivors based on the COR theory. This cross-sectional telephone survey enrolled 199 COVID-19 patients (Mean age = 42.7; 53.3% females) 6 months after their hospital discharge in five Chinese cities (i.e., Wuhan, Shenzhen, Zhuhai, Dongguan, and Nanning). The results showed that 7% of participants were classified as having probable PTSD. The significant potential factors relating to PTSD symptoms included socio-demographic status, hospitalization experiences, post-hospitalization experiences, and psychological status. Besides, the proposed statistical mediation model based on the COR framework showed good model fit, χ2(df) = 17.286 (5), p = 0.004, CFI = 0.962, NNFI = 0.951, RMSEA = 0.077. Perceived resource loss/gain fully mediated the association between exposure to other patients' suffering during hospitalization and PTSD symptoms, and partially mediated the relationships from somatic symptoms/perceived impact of being infected with COVID-19 after discharge to PTSD symptoms. On the other hand, resilience was a full mediator in the relationship from ICU experience to PTSD symptoms and a partial mediator in the relationship from perceived impact to PTSD symptoms. The results provide preliminary support on applying the COR theory to understand the factors of PTSD symptoms among COVID-19 survivors. Interventions to reduce PTSD symptoms in this population can be developed based on the modifiable psychosocial mediators.

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.

Increasing evidence suggests that dysregulated immune responses are associated with the clinical outcome of coronavirus disease 2019 (COVID-19). Nucleocapsid protein (NP)-, spike (S)-, receptor binding domain (RBD)- specific immunoglobulin (Ig) isotypes, IgG subclasses and neutralizing antibody (NAb) were analyzed in 123 serum from 63 hospitalized patients with severe, moderate, mild or asymptomatic COVID-19. Mild to modest correlations were found between disease severity and antigen specific IgG subclasses in serum, of which IgG1 and IgG3 were negatively associated with viral load in nasopharyngeal swab. Multiple cytokines were significantly related with antigen-specific Ig isotypes and IgG subclasses, and IL-1β was positively correlated with most antibodies. Furthermore, the old patients (≥ 60 years old) had higher levels of chemokines, increased NAb activities and SARS-CoV-2 specific IgG1, and IgG3 responses and compromised T cell responses compared to the young patients (≤ 18 years old), which are related with more severe cases. Higher IgG1 and IgG3 were found in COVID-19 patients with comorbidities while biological sex had no effect on IgG subclasses. Overall, we have identified diseases severity was related to higher antibodies, of which IgG subclasses had weakly negative correlation with viral load, and cytokines were significantly associated with antibody response. Further, advancing age and comorbidities had obvious effect on IgG1 and IgG3.