Immunogenicity of convalescent and vaccinated sera against clinical isolates of ancestral SARS-CoV-2, Beta, Delta, and Omicron variants
Bibliographic record
Abstract
Background: SARS-CoV-2 Omicron variant of concern (VOC) has evolved multiple mutations within the spike protein, raising concerns of increased antibody evasion. In this study, we assessed the neutralization potential of COVID-19 convalescent sera and sera from vaccinated individuals against ancestral SARS-CoV-2 and VOCs. Methods: The neutralizing activity of sera from 65 coronavirus disease (COVID-19) vaccine recipients and convalescent individuals against clinical isolates of ancestral SARS-CoV-2 and Beta, Delta, and Omicron VOCs was assessed using a micro-neutralization assay. Findings: Convalescent sera from unvaccinated individuals infected by the ancestral virus demonstrated reduced neutralization against Beta and Omicron VOCs. Sera from individuals that received three doses of the Pfizer or Moderna vaccines demonstrated reduced neutralization of the Omicron variant relative to ancestral SARS-CoV-2. Sera from individuals that were naturally infected with ancestral SARS-CoV-2 and subsequently received two doses of the Pfizer vaccine induced significantly higher neutralizing antibody levels against ancestral virus and all VOCs. Infection alone, either with ancestral SARS-CoV-2 or the Delta variant, was not sufficient to induce high neutralizing antibody titers against Omicron. Conclusions: In summary, we demonstrate that convalescent and vaccinated sera display varying levels of SARS-CoV-2 VOC neutralization. Data from this study will inform booster vaccination strategies against SARS-CoV-2 VOCs. Funding: This research was funded by the Canadian Institutes of Health Research (CIHR). VIDO receives operational funding from the Government of Saskatchewan through Innovation Saskatchewan and the Ministry of Agriculture and from the Canada Foundation for Innovation through the Major Science Initiatives for its CL3 facility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".