Severe disease is not essential for a high neutralizing antibody response post-SARS-CoV-2 infection
Bibliographic record
Abstract
Neutralizing antibody responses correlate with protection from SARS-CoV-2 infection, yet higher neutralizing responses associate with more severe disease. Whether people without severe disease can also develop strong neutralizing responses to infection, and the pathways involved, is less clear. We performed a proteomic analysis on sera from 71 individuals infected with ancestral SARS-CoV-2, enrolled during the first South African infection wave. We determined disease severity by whether participants required supplemental oxygen and measured neutralizing antibody levels at convalescence. High neutralizing antibodies were associated with high disease severity, yet 40% of participants with lower disease severity had neutralizing antibody levels comparable to those with severe disease. We found 130 differentially expressed proteins between high and low neutralizers and 40 between people with high versus low disease severity. Five proteins overlapped, including furin, a protease which enhances SARS-CoV-2 infection. High neutralizers with non-severe disease had similar levels of differentially expressed neutralization response proteins to high neutralizers with severe disease, yet similar levels of differentially expressed disease severity proteins to participants with non-severe disease. Furthermore, we could reasonably predict who developed a strong neutralizing response based on a single protein, HSPA8, involved in clathrin pit uncoating. These results indicate that a strong antibody response does not always require severe disease and may involve different pathways.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".