Durability of DNA-LNP and mRNA-LNP Vaccine-Induced Immunity Against SARS-CoV-2 XBB.1.5
Bibliographic record
Abstract
Abstract mRNA-lipid nanoparticle (LNP) vaccines induce robust adaptive immune responses and have proven highly effective against SARS-CoV-2. However, their long-term effectiveness is limited by waning humoral responses, which decline substantially within the first six months post-boost vaccination. DNA-LNPs are being investigated as an alternative vaccine platform, offering prolonged antigen expression and robust immunity. Here, we present the first comparison of SARS-CoV-2 DNA- and mRNA-LNP vaccines in a long-term in vivo challenge model. Both nucleic acid platforms induced strong neutralizing antibody responses and conferred equivalent protection in Syrian hamsters challenged three weeks post-boost. Notably, DNA-LNP vaccination maintained high binding and neutralizing antibody titers six months post-boost, whereas mRNA-LNPs exhibited a marked decline. Correspondingly, while DNA-LNPs completely protected from weight loss, viral replication, and lung pathology at this late timepoint, mRNA-LNP vaccination conferred minimal protection. These findings demonstrate that DNA-LNPs can sustain durable immunity, highlighting their potential as a next-generation vaccine platform that could reduce the need for frequent boosters.
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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) | 0.000 | 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 | 0.000 |
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".