Convalescent plasma donors show enhanced cross‐reactive neutralizing antibody response to antigenic variants of SARS‐CoV‐2 following immunization
Bibliographic record
Abstract
BACKGROUND: The therapeutic benefit of convalescent plasma (CP) therapy to treat COVID-19 may derive from neutralizing antibodies (nAbs) to SARS-CoV-2. To investigate the effects of antigenic variation on neutralization potency of CP, we compared nAb titers against prototype and recently emerging strains of SARS-CoV-2, including Delta and Omicron, in CP donors previously infected with SARS-CoV-2 before and after immunization. METHODS AND MATERIALS: Samples were assayed from previously SARS-CoV-2 infected donors before (n = 17) and after one (n = 43) or two (n = 71) doses of Astra-Zeneca or Pfizer vaccinations. Ab titers against Wuhan/wild type (WT), Alpha, Beta, and Delta SARS-CoV-2 strains were determined by live virus microneutralization assay while titers to Omicron used a focus reduction neutralization test. Anti-spike antibody was assayed by Elecsys anti-SARS-CoV-2 quantitative spike assay (Roche). RESULTS: Unvaccinated donors showed a geometric mean titer (GMT) of 148 against WT, 80 against Alpha but mostly failed to neutralize Beta, Delta, and Omicron strains. Contrastingly, high GMTs were observed in vaccinated donors against all SARS-CoV-2 strains after one vaccine dose (WT:703; Alpha:692; Beta:187; Delta:215; Omicron:434). By ROC analysis, reactivity in the Roche quantitative Elecsys spike assay of 20,000 U/mL was highly predictive of donations with nAb titers of ≥1:640 against Delta (90% sensitivity; 97% specificity) and ≥1:320 against Omicron (89% sensitivity; 81% specificity). DISCUSSION: Vaccination of previously infected CP donors induced high levels of broadly neutralizing antibodies against circulating antigenic variants of SARS-CoV-2. High titer donations could be reliably identified by automated quantitative anti-spike antibody assay, enabling large-scale preselection of high-titer convalescent plasma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".