SARS-COV-2 Recombinant Receptor-Binding-Domain (RBD) Induces Neutralising Antibodies Against Variant Strains of SARS-CoV-2 and SARS-CoV-1
Bibliographic record
Abstract
Abstract SARS-CoV-2 is the etiological agent of COVID19. There are currently several licensed vaccines approved for human use and most of them are targeting the spike protein (or virion) in the virion envelope to induce protective immunity. Recently, variants that spread more quickly have emerged. There is evidence that some of these variants are less sensitive to neutralization in vitro , but it is not clear whether they can evade vaccine induced protection. In this study, we tested the utility of SARS-CoV-2 spike RBD as a vaccine antigen and explore the effect of formulation with Alum/MPLA or AddaS03 adjuvants. Our results indicate RBD induces high titers of neutralizing antibodies and activates strong cellular immune responses. There is also significant cross-neutralisation of variants B1.1.7 and B.1.351 and to a lesser extent, SARS-CoV-1. These results indicate that recombinant RBD can be a viable candidate as a stand-alone vaccine or as a booster shot to diversify our strategy for COVID19 protection.
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 | 0.001 |
| 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".