Evaluation of Different Strategies for Post-Exposure Treatment of Ebola Virus Infection in Rodents
Bibliographic record
Abstract
Zaire Ebola virus (ZEBOV) is a pathogen that causes severe hemorrhagic fever in humans and non-human primates. There are currently no licensed vaccines or approved treatments available against ZEBOV infections. The goal of this work was to evaluate different treatment strategies in conjunction with a replication deficient, recombinant human adenovirus serotype 5-based vaccine expressing the Zaire Ebola virus glycoprotein (Ad-CAGoptZGP) in Ebola infected mice and guinea pigs.Guinea pigs were treated with Ad-CAGoptZGP in combination with different treatment strategies after challenge with guinea pig adapted-ZEBOV (GA-ZEBOV). B10.BR mice were used to further characterize efficacy and immune responses following co-administration of Ad-CAGoptZGP with the most effective treatment: AdHu5 expressing recombinant IFN-α (hereafter termed DEF201) after challenge with a lethal dose of mouse adapted-ZEBOV (MA-ZEBOV).In mice, DEF201 treatment was able to elicit full protection against a lethal dose of MA-ZEBOV when administered 30 minutes after infection. In guinea pigs the Ad-CAGoptZGP and DEF201 combination therapy elicited full protection when treated 30 minutes post-exposure and were a superior treatment to Ad-CAGoptZGP supplemented with recombinant IFN-α protein. Further analysis of the immune response revealed that addition of DEF201 to Ad-CAGoptZGP enhances the resulting adaptive immune response against ZGP. The results highlight the importance of the innate immune response in the prevention of ZEBOV pathogenesis and support further development of the Ad-CAGoptZGP with DEF201 treatment combination for post-exposure therapy against ZEBOV infection.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".