Universal antibody targeting the highly conserved fusion peptide provides cross-protection in mice
Bibliographic record
Abstract
Influenza is a major public health concern causing millions of hospitalizations every year. The current vaccines need annual updating based on prediction of likely strains in the upcoming season. However, mismatches between vaccines and the actual circulating viruses can occur, reducing vaccine effectiveness significantly because of the remarkably high rate of mutation in the viral glycoprotein, hemagglutinin (HA). Clearly, it would be of great interest to determine the potential role of universally conserved epitopes in inducing protective immunity. Here, an antibody against the 14-aa fusion peptide sequence at the N-terminus of the HA2 subunit (Uni-1) was investigated for its ability to elicit antibody-dependent cellular cytotoxicity (ADCC) in vitro and cross-protection against lethal infection in animals. Uni-1, known to neutralize influenza type A (IAV) in vitro, was found to induce strong ADCC against diverse influenza viruses, including human and avian IAVs and both lineages of type B (IBV). The ADCC effects against human IAVs by Uni-1 was comparable to ADCC induced by well-characterized antibodies, F10 and FI6V3. Importantly, mice treated with Uni-1 were protected against lethal challenge of IAV and IBV. These results revealed the versatile effector functions of this universal antibody against markedly diverse strains of both IAV and IBV.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".