Reducing the priming dose of a SARS CoV-2 vaccine improves vaccine-elicited immunity
Bibliographic record
Abstract
Abstract SARS-CoV-2 caused a global pandemic that has killed over 2 million people. While several vaccine candidates have received emergency use authorization, there is still limited vaccine availability and lack of knowledge on optimal vaccine dosing. A recent AstraZeneca vaccine study with an adenovirus vector suggested that priming with a low dose (LD) is superior than priming with a standard dose (SD). We extended these results to a murine vaccination model to further understand the mechanism of how limiting the priming dose affects vaccine-elicited immunity. We first primed C57BL/6 mice intramuscularly with an adenovirus-based vaccine expressing SARS CoV-2 spike (Ad5-spike), either with a LD (106 PFU) or a SD (109 PFU), followed by a SD boost three weeks later. An initial priming with a SD resulted in a higher magnitude of adaptive immune responses relative to an initial priming with a LD, consistent with the notion that adaptive responses are proportional to the priming antigen dose. However, T cell responses generated by a LD prime exhibited more rapid central memory differentiation, suggesting that they could display improved recall expansion following subsequent booster immunization. Interestingly, mice that were primed with a LD exhibited significantly more potent anamnestic T cell responses upon boosting, relative to mice that were primed and boosted with a SD. Antibody responses were also significantly improved in the LD/SD vaccine regimen. Overall, our data suggest that limiting the priming dose may offer a substantial long-term benefit for SARS CoV-2 vaccines. These findings may be useful for improving vaccine availability and also for the rational design of prime-boost vaccine regimens for SARS CoV-2 and other diseases.
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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 | 0.000 | 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 | 0.000 |
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".