Contemporaneous SARS-CoV-2-Neutralizing Antibodies Mediated by N-glycan Shields
Bibliographic record
Abstract
Mutations and the glycosylation of epitopes can convert immunogenic epitopes into non-immunogenic ones via natural selection or evolutionary pressure, thereby decreasing their sensitivity to neutralizing antibodies. Based on Thomas Francis's theory, memory B and T cells induced during primary infections or vaccination will freeze the new mutated epitopes specific to naïve B and T cells from the repertoire. On this basis, some researchers argue that the current vaccines derived from the previous strains of the SARS-CoV-2 virus do not increase immunity and may also prevent the immune response against new epitopes. However, evidence shows that even if the binding affinity is reduced, the previous antibodies or T cell receptors (TCRs) can still bind to this new epitope of the Beta, Gamma, and Delta variant if their concentration is high enough (from a booster injection) and neutralize the virus. This paper presents some convincing immunological reasons that may challenge this theory and argue for the continuation of universal vaccination to prevent further mutations of the SARS-CoV-2 virus. Simultaneously, the information presented can be used to develop vaccines that target novel epitopes or create new recombinant drugs that do not lose their effectiveness when the virus mutates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".