T-cell immunity to SARS-CoV-2: what if the known best is not the optimal course for the long run? Adapting to evolving targets
Bibliographic record
Abstract
Humanity did surprisingly well so far, considering how unprepared it was to respond to the coronavirus disease 2019 (COVID-19) threat. By blending old and ingenious new technology in the context of the accumulated knowledge on other human coronaviruses, several vaccine candidates were produced and tested in clinical trials in record time. Today, five vaccines account for the bulk of the more than 13 billion doses administered worldwide. The ability to elicit biding and neutralizing antibodies most often against the spike protein is a major component of the protection conferred by immunization but alone it is not enough to limit virus transmission. Thus, the surge in numbers of infected individuals by newer variants of concern (VOCs) was not accompanied by a proportional increase in severe disease and death rate. This is likely due to antiviral T-cell responses, whose evasion is more difficult to achieve. The present review helps navigating the very large literature on T cell immunity induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination. We examine the successes and shortcomings of the vaccinal protection in the light of the emergence of VOCs with breakthrough potential. SARS-CoV-2 and human beings will likely coexist for a long while: it will be necessary to update existing vaccines to improve T-cell responses and attain better protection against COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".