The Fc-effector function of COVID-19 convalescent plasma contributes to SARS-CoV-2 treatment efficacy in mice
Bibliographic record
Abstract
COVID-19 convalescent plasmas (CCPs) are chosen for plasma therapy based on neutralizing titers and anti-Spike immunoglobulin levels. However, CCP characteristics that promote SARS-CoV-2 control are complex and incompletely defined. Using an in vivo imaging approach, we demonstrate that CCPs with low neutralizing (ID 50 ≤ 1:250), but moderate to high Fc-effector activity, in contrast to those with poor Fc function, delay mortality and/or improve survival of SARS-CoV-2-challenged K18-hACE2 mice. The impact of innate immune cells on CCP efficacy depended on their residual neutralizing activity. Fractionation of a selected CCP revealed that IgG and Ig(M + A) were required during therapy, but the IgG fraction alone sufficed during prophylaxis. Finally, despite reduced neutralization, ancestral SARS-CoV-2-elicited CCPs significantly delayed Delta and Beta-induced mortality suggesting that Fc-effector functions contribute to immunity against VOCs. Thus, Fc activity of CCPs provide a second line of defense when neutralization is compromised and can serve as an important criterion for CCP selection.
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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 | 0.000 |
| 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 | 0.001 |
| 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".