ABSTRACT
Recent WHO vaccination guidance no longer recommends COVID-19 vaccination beyond the first booster in low risk population, citing high population-level hybrid immunity due to widespread omicron infections.1 Although SARS-CoV-2 infection confers durable protection against reinfection,2-4 it may also produce immune imprinting, which skews subsequent immune response to variant antigens toward the first-exposed antigen based on the antigenic distance.5,6 China has the earliest and exclusively Wuhan-Hu-1(WH1)-imprinted population before the 2022 nation-wide omicron outbreak,7 which offers a unique opportunity to study long-term immune imprinting between most antigenically distant strains. Here, we assessed pseudovirus neutralization activity and anti-WH1 receptor binding domain (RBD) antibodies in 4 Chinese cohorts with hybrid or vaccine-only imprinting, or naïve to SARS-CoV-2 prior to omicron BF.7 infection. Both hybrid and vaccine-only imprinting augmented post-infection serum neutralization of WH1 and omicron sub-variants BF.7/BQ.1.1/XBB.1.5 comparing to naïve background. Feedback from pre-existing high-affinity antibodies limited the magnitude of humoral immune response to omicron infection without compromising protection, while antigenic seniority of pre-existing cross-reactive B cells only slightly reduces forward neutralization breadth in hybrid- and RBD vaccine-imprinted participants. Our results support the effectiveness of hybrid immunity against omicron reinfection in long-term imprinted population and provide immunological basis for similar epidemiological findings.8-10
Subject(s)
COVID-19ABSTRACT
Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.
Subject(s)
COVID-19ABSTRACT
Objectives: The pandemic of the coronavirus disease 2019 (COVID-19) continuously poses a serious threat to public health, highlighting an urgent need for simple and efficient early detection and prediction. Methods: We comprehensively investigated and reanalyzed the published indexes and models for predicting severe illness among COVID‑19 patients in our dataset, and validated them on an independent dataset. Results: 696 COVID-19 cases in the discovery stage and 337 patients in the validation stage were involved. The AuROC of neutrophil to lymphocyte ratio (NLR) (0.782) was significantly higher than that of the other 11 independent risk indexes in severe outcome prediction. The combination of NLR and oxygen saturation (SaO2) (NLR+SaO2) showed the biggest AuROC calculations with a value of 0.901; with a cut-off value of 0.532, it exhibited 84.2% sensitivity, 88.4% specificity and 86.8% correct classification ratio. Moreover, we first identified that principal component analysis (PCA) is an effective tool to predict the severity of COVID-19. We obtained 86.5% prediction accuracy with 86% sensitivity when PCA was applied to predict severe illness. In addition, to evaluate the performance of NLR+SaO2 and PCA, we compared them with currently published predictive models in the same dataset. Conclusions: It showed that NLR+SaO2 is an appropriate and promising method for predicting severe illness, followed by PCA. We then validated the results on an independent dataset and revealed that they remained robust accuracy in outcome prediction. This study is significant for early treatment, intervention, triage and saving limited resources.