Circulating neutralizing antibodies and SARS-CoV-2 variant replication following postvaccination infections
Bibliographic record
Abstract
The effect of preexisting neutralizing antibodies (NAb) on SARS-CoV-2 shedding in postvaccination infection (PVI) is not well understood. We characterized viral shedding longitudinally in nasal specimens in relation to baseline (pre/periinfection) serum NAb titers in 125 participants infected with SARS-CoV-2 variants. Among 68 vaccinated participants, we quantified the effect of baseline NAb titers on maximum viral RNA titers and infectivity duration. Baseline NAbs were higher and targeted a broader range of variants in participants with monovalent ancestral booster vaccinations compared with those with a primary vaccine series. In Delta infections, baseline NAb titers targeting Delta or Wuhan-Hu-1 correlated negatively with maximum viral RNA. Per log10 increase in Delta-targeting baseline NAb IC50, maximum viral load was reduced -2.43 (95% CI: -3.76, -1.11) log10 nucleocapsid copies, and infectious viral shedding was reduced -2.79 (95% CI: -4.99, -0.60) days. Conversely, in Omicron infections (BA.1, BA.2, BA.4, or BA.5), baseline NAb titers against Omicron lineages or Wuhan-Hu-1 did not predict viral outcomes. Our results provide robust estimates of the effect of baseline NAbs on the magnitude and duration of nasal viral replication after PVI (albeit with an unclear effect on transmission) and show how immune escape variants efficiently evade these modulating effects.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".