Potent neutralizing monoclonal antibodies against Ebola virus isolated from vaccinated donors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ebola virus (EBOV) can cause severe hemorrhagic fever in humans, and no approved treatment is currently available. Although several antibodies have achieved good protection in animal models, the potential emerging isolates of ebolavirus and the unknown effects of experimental antibodies in humans underscore the need to develop additional antibodies to address the threat of Ebola. Here, we isolated a series of memory B cell-derived monoclonal antibodies from healthy Chinese adults vaccinated with Ad5-EBOV. These antibodies were encoded by diverse germline genes and had high levels of somatic hypermutation. Most antibodies were cross-reactive and could bind at least two ebolavirus glycoproteins (GPs). Seven neutralizing antibodies were identified using HIV-EBOV GP-Luc pseudovirus, and they effectively neutralized authentic EBOV. In particular, monoclonal antibody 2G1 exhibited potent cross-neutralization against HIV-EBOV/SUDV/BDBV GP-Luc bearing different ebolavirus GPs. We used truncated GPs, competition assays, and software prediction to analyze seven neutralizing antibodies, which bound four different epitopes on GP. Importantly, three of these antibodies provided complete protection in mice when administered one day post-infection. Our study expands the list of candidate antibodies and the options for successfully treating ebolavirus infection.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it