Pandemic H1N1 Influenza Infection and Vaccination in Humans Induces Cross-Protective Antibodies that Target the Hemagglutinin Stem
Bibliographic record
Abstract
Most monoclonal antibodies (mAbs) generated from humans infected or vaccinated with the 2009 pandemic H1N1 (pdmH1N1) influenza virus targeted the hemagglutinin (HA) stem. These anti-HA stem mAbs mostly used IGHV1-69 and bound readily to epitopes on the conventional seasonal influenza and pdmH1N1 vaccines. The anti-HA stem mAbs neutralized pdmH1N1, seasonal influenza H1N1 and avian H5N1 influenza viruses by inhibiting HA-mediated fusion of membranes and protected against and treated heterologous lethal infections in mice with H5N1 influenza virus. This demonstrated that therapeutic mAbs could be generated a few months after the new virus emerged. Human immunization with the pdmH1N1 vaccine induced circulating antibodies that when passively transferred, protected mice from lethal, heterologous H5N1 influenza infections. We observed that the dominant heterosubtypic antibody response against the HA stem correlated with the relative absence of memory B cells against the HA head of pdmH1N1, thus enabling the rare heterosubtypic memory B cells induced by seasonal influenza and specific for conserved sites on the HA stem to compete for T-cell help. These results support the notion that broadly protective antibodies against influenza would be induced by successive vaccination with conventional influenza vaccines based on subtypes of HA in viruses not circulating in humans.
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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".