Role of Antibodies in Protection Against Ebola Virus in Nonhuman Primates Immunized With Three Vaccine Platforms
Bibliographic record
Abstract
Background: Several vaccine platforms have been successfully evaluated for prevention of Ebola virus (EBOV) disease (EVD) in nonhuman primates and humans. Despite remarkable efficacy by multiple vaccines, the immunological correlates of protection against EVD are incompletely understood. Methods: We systematically evaluated the antibody response to various EBOV proteins in 79 nonhuman primates vaccinated with various EBOV vaccine platforms. We evaluated the serum immunoglobulin (Ig)G titers against EBOV glycoprotein (GP), the ability of the vaccine-induced antibodies to bind GP at acidic pH or to displace ZMapp, and virus neutralization titers. The correlation of these outcomes with survival from EVD was evaluated by appropriate statistical methods. Results: Irrespective of the vaccine platform, protection from EVD strongly correlated with anti-GP IgG titers. The GP-directed antibody levels required for protection in animals vaccinated with virus-like particles (VLPs) lacking nucleoprotein (NP) was significantly higher than animals immunized with NP-containing VLPs or adenovirus-expressed GP, platforms that induce strong T-cell responses. Furthermore, protective immune responses correlated with anti-GP antibody binding strength at acidic pH, neutralization of GP-expressing pseudovirions, and the ability to displace ZMapp components from GP. Conclusions: These findings suggest key quantitative and qualitative attributes of antibody response to EVD vaccines as potential correlates of protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".