Sensitive detection of anti‐spike antibodies enables improved understanding of SARS‐CoV‐2 pathogenesis
Bibliographic record
Abstract
Mass vaccination of the global population against SARS-CoV-2 will, we hope, turn the tide against this devastating pandemic. To complement vaccinations, better tools are needed to enable viral infections and immunological protection to be monitored. Accurate tools provide sound data for informed decision-making at many levels, from personal to governmental. The measurement of viral RNA is currently routinely used to detect active infections, but only gives a positive result during infection and is unable to reveal historic infections. Tests involving a detection of SARS-CoV-2-specific antibodies can reveal prior exposures to virus and can measure anti-viral immune responses induced after natural infection or after vaccination. They may eventually also be used to predict an individual's likelihood of becoming re-infected. Here, we report on the development of a sensitive ELISA technique to detect multiple isotypes of antibodies against the spike glycoprotein, in samples of both serum and saliva. This paper provides an important step towards understanding the immune response to SARS-CoV-2 and may therefore eventually help us to effectively control it.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".