Cytidine deamination-induced perpetual immunity to SAR-CoV-2 infection is a potential new therapeutic target
Bibliographic record
Abstract
As the world is racing to develop perpetual immunity to the SARS-CoV-2 virus. The emergence of new viral strains, together with vaccination and reinfections, are all contributing to a long-term immunity against the deadly virus that has taken over the world since its introduction to humans in late December 2019. The discovery that more than 95 percent of people who recovered from COVID-19 had long-lasting immunity and that asymptomatic people have a different immune response to SARS-CoV-2 than symptomatic people has shifted attention to how our immune system initiates such diverse responses. These findings have provided reason to believe that SARS-CoV-2 days are numbered. Hundreds of research papers have been published on the causes of long-lasting immune responses and variations in the numbers of different immune cell types in COVID 19 survivors, but the main reason of these differences has still not been adequately identified. In this article, we focus on the activation-induced cytidine deaminase (AID), which initiates molecular processes that allow our immune system to generate antibodies against SARS-CoV-2. To establish lasting immunity to SARS-CoV-2, we suggest that AID could be the key to unlocking 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".