Spike and nucleocapsid antibody dynamics following <scp>SARS</scp>‐<scp>CoV</scp>‐2 infection and vaccination: Implications for sourcing <scp>COVID</scp>‐19 convalescent plasma from routinely collected blood donations
Bibliographic record
Abstract
BACKGROUND: COVID-19 convalescent plasma (CCP) remains a treatment option for immunocompromised patients; however, the current FDA qualification threshold of ≥200 BAU/mL of spike antibody appears to be relatively low. We evaluated the levels of binding (bAb) and neutralizing antibodies (nAb) on serial samples from repeat blood donors who were vaccinated and/or infected to inform criteria for qualifying CCP from routinely collected plasma components. METHODS: Donors were categorized into four groups: (1) infected, then vaccinated, (2) vaccinated then infected during the delta, or (3) omicron waves, (4) vaccinated without infection. IgG Spike and total Nuclecapsid bAb were measured, along with S variants and nAb titers using reporter viral particle neutralization. RESULTS: Mean S IgG bAb peaks after infection alone were lower than after primary and booster vaccinations, and higher after delta and omicron infection in previously vaccinated donors. Half-lives for S IgG ranged from 34 to 66 days after first infection/vaccination events and up to 108 days after second events. The levels of S IgG bAb and nAb were similar across different variants, except for omicron, which were lower. Better correlations of nAb with bAb were observed at higher levels (hybrid immunity) than at the current FDA CCP qualifying threshold. DISCUSSION: Routine plasma donations from donors with hybrid immunity had high S bAb and potent neutralizing activity for 3-6 months after infection. In donations with high (>4000 BAU/mL) S IgG, >95% had high nAb titers (>500) against ancestral and variant S, regardless of COVID-19 symptoms. These findings provide the basis for test-based criteria for qualifying CCP from routine blood donations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".