Mucosal chemokine adjuvant enhances synDNA vaccine-mediated responses to SARS-CoV-2 and provides heterologous protection in vivo
Bibliographic record
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic has claimed more than 5 million lives. Emerging variants of concern (VOCs) continually challenge viral control. Directing vaccine-induced humoral and cell-mediated responses to mucosal surfaces may enhance vaccine efficacy. Here we investigate the immunogenicity and protective efficacy of optimized synthetic DNA plasmids encoding wild-type severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein (pS) co-formulated with the plasmid-encoded mucosal chemokine cutaneous T cell-attracting chemokine (pCTACK; CCL27). pCTACK-co-immunized animals exhibit increased spike-specific antibodies at the mucosal surface and increased frequencies of interferon gamma (IFNγ) + CD8 + T cells in the respiratory mucosa. pCTACK co-immunization confers 100% protection from heterologous Delta VOC challenge. This study shows that mucosal chemokine adjuvants can direct vaccine-induced responses to specific immunological sites and have significant effects on heterologous challenge. Further study of this unique chemokine-adjuvanted vaccine approach in the context of SARS-CoV-2 vaccines is likely important.
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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".