Antibody Responses in SARS-CoV-2-Exposed and/or Vaccinated Individuals Target Conserved Epitopes from Multiple CoV-2 Antigens
Bibliographic record
Abstract
There is a need to investigate novel strategies in order to create an effective, broadly protective vaccine for current and future severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks. The currently available vaccines demonstrate compromised efficacy against emerging SARS-CoV-2 variants of concern (VOCs), short-lived immunity, and susceptibility to immune imprinting due to frequent boosting practices. In this study, we examined the specificity of cross-reactive IgG antibody responses in mRNA-vaccinated, AstraZeneca-vaccinated, and unvaccinated donors to identify potentially conserved, cross-reactive epitopes to target in order to create a broadly protective SARS-CoV-2 vaccine. Our study provides evidence for cross-reactive IgG antibodies specific to eight different spike (S) variants. Furthermore, the specificities of these cross-variant IgG antibody titers were associated to some extent with spike S1- and S2-subunit-derived epitopes P1 and P2, respectively. In addition, nucleocapsid (N)- and membrane (M)-specific IgG antibody titers correlated with N- and M-derived epitopes conserved across beta-CoVs, P3–7. This study reveals conserved epitopes of viral antigens, targeted by natural and/or vaccine-induced human immunity, for future designs of next-generation COVID-19 vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".