Resistance of <scp>SARS‐CoV</scp>‐2 beta and gamma variants to plasma collected from Canadian blood donors during the spring of 2020
Bibliographic record
Abstract
Abstract Background This pilot study assesses the ability of plasma collected from Canadian blood donors in the first wave of the SARS‐CoV‐2 pandemic to neutralize later SARS‐CoV‐2 variants of concern (VOCs). Study design and methods A repeated cross‐sectional design was used, and a random cross‐sectional sample of all available Canadian Blood Services retention samples (n = 1500/month) was drawn monthly for April and May of 2020. Qualitative IgG analysis was performed on aliquots of specimens using anti‐spike, anti‐receptor binding domain, and anti‐nucleocapsid protein enzyme‐linked immunosorbent assays as well as the Abbott Architect SARS CoV‐2 IgG assay (Abbott Laboratories) against the anti‐nucleocapsid protein. Selected plasma specimens were then assessed for neutralization against VOCs using pseudotyped lentivirus inhibition assays as well as plaque reduction neutralization test 50% (PRNT50). Results Six specimens with a high neutralizing titer against wild‐type SARS‐CoV‐2 and three specimens with a low neutralizing titer against wild‐type SARS‐CoV‐2 were chosen for further analysis against VOCs. Four of six high neutralizing titer specimens had a reduced neutralizing capacity against beta VOCs by both neutralization methods. Three of six high neutralizing titer specimens had reduced neutralization capacity against gamma VOCs. Conclusions This preliminary data can be used as a justification for limiting the use of first wave plasma products in upcoming clinical trials but cannot be used to speculate on general trends in the immunity of Canadian blood donors to SARS‐CoV‐2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".