A single immunization with optimized DNA vaccines protects against lethal Ebola virus challenge in mice (VAC8P.1057)
Bibliographic record
Abstract
Abstract Ebola is a hemorrhagic fever virus that is responsible for the severe, ongoing outbreak in West Africa that has infected >20000 people, exhibiting >37% mortality. Infection control has been complicated by a lack of approved vaccines and therapies and recent figures suggest that the outbreak is more severe than previously thought. We generated 3 DNA vaccines expressing Zaire ebolavirus (EBOV) glycoprotein (GP): 2 vaccines designed on consensus alignments of EBOV GPs (1976-2014) and a 3rd matched construct to a 2014 Guinea strain. BALB/c mice received 40ug of each vaccine, delivered by IM injection followed by electroporation. Strong T cell responses were detected for all 3 vaccines, including GP-specific CD4+ and CD8+ T cells. To broaden efficacy against emerging EBOV strains, we also co-administered DNA vaccines in 2 or 3 construct formulations. Groups of 10 BALB/c mice were challenged with a lethal dose of a heterologous mouse-adapted Mayinga 1976 EBOV strain. The two consensus DNA vaccines, and combinations of 2 and 3 vaccines were 100% protective in mice. The matched vaccine afforded 90% protection. Total GP-specific IgG antibody levels were high in all surviving animals and low in unprotected animals suggesting that titers may indicate therapeutic efficacy. All three DNA vaccines are immunogenic and no antigen interference was observed. These experiments in mice support further studies in non-human primates towards continued development of this potential vaccine platform.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".