Guardians of the oral and nasopharyngeal galaxy: <scp>IgA</scp> and protection against <scp>SARS‐CoV</scp>‐2 infection*
Bibliographic record
Abstract
In early 2020, a global emergency was upon us in the form of the coronavirus disease 2019 (COVID-19) pandemic. While horrific in its health, social and economic devastation, one silver lining to this crisis has been a rapid mobilization of cross-institute, and even cross-country teams that shared common goals of learning as much as we could as quickly as possible about the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and how the immune system would respond to both the virus and COVID-19 vaccines. Many of these teams were formed by women who quickly realized that the classical model of "publish first at all costs" was maladaptive for the circumstances and needed to be supplanted by a more collaborative solution-focused approach. This review is an example of a collaboration that unfolded in separate countries, first Canada and the United States, and then also Israel. Not only did the collaboration allow us to cross-validate our results using different hands/techniques/samples, but it also took advantage of different vaccine types and schedules that were rolled out in our respective home countries. The result of this collaboration was a new understanding of how mucosal immunity to SARS-CoV-2 infection vs COVID-19 vaccination can be measured using saliva as a biofluid, what types of vaccines are best able to induce (limited) mucosal immunity, and what are potential correlates of protection against breakthrough infection. In this review, we will share what we have learned about the mucosal immune response to SARS-CoV-2 and to COVID-19 vaccines and provide a perspective on what may be required for next-generation pan-sarbecoronavirus vaccine approaches.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".