The Ability of Zika virus Intravenous Immunoglobulin to Protect From or Enhance Zika Virus Disease
Bibliographic record
Abstract
The closely related flaviviruses, dengue and Zika, cause significant human disease throughout the world. While cross-reactive antibodies have been demonstrated to have the capacity to potentiate disease or mediate protection during flavivirus infection, the mechanisms responsible for this dichotomy are still poorly understood. To understand how the human polyclonal antibody response can protect against, and potentiate the disease in the context of dengue and Zika virus infection we used intravenous hyperimmunoglobulin (IVIG) preparations in a mouse model of the disease. Three IVIGs (ZIKV-IG, Control-Ig and Gamunex®) were evaluated for their ability to neutralize and/or enhance Zika, dengue 2 and 3 virusesin vitro. The balance between virus neutralization and enhancement provided by thein vitroneutralization data was used to predict the IVIG concentrations which could protect or enhance Zika, and dengue 2 diseasein vivo. Using this approach, we were able to define the uniquein vivodynamics of complex polyclonal antibodies, allowing for both enhancement and protection from flavivirus infection. Our results provide a novel understanding of how polyclonal antibodies interact with viruses with implications for the use of polyclonal antibody therapeutics and the development and evaluation of the next generation flavivirus vaccines.
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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 | 0.000 |
| 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".