Induction of broadly cross-reactive antibody responses to the influenza HA stem region following H5N1 vaccination in humans
Bibliographic record
Abstract
The emergence of pandemic influenza viruses poses a major public health threat. Therefore, there is a need for a vaccine that can induce broadly cross-reactive antibodies that protect against seasonal as well as pandemic influenza strains. Human broadly neutralizing antibodies directed against highly conserved epitopes in the stem region of influenza virus HA have been recently characterized. However, it remains unknown what the baseline levels are of antibodies and memory B cells that are directed against these conserved epitopes. More importantly, it is also not known to what extent anti-HA stem B-cell responses get boosted in humans after seasonal influenza vaccination. In this study, we have addressed these two outstanding questions. Our data show that: (i) antibodies and memory B cells directed against the conserved HA stem region are prevalent in humans, but their levels are much lower than B-cell responses directed to variable epitopes in the HA head; (ii) current seasonal influenza vaccines are efficient in inducing B-cell responses to the variable HA head region but they fail to boost responses to the conserved HA stem region; and (iii) in striking contrast, immunization of humans with the avian influenza virus H5N1 induced broadly cross-reactive HA stem-specific antibodies. Taken together, our findings provide a potential vaccination strategy where heterologous influenza immunization could be used for increasing the levels of broadly neutralizing antibodies and for priming the human population to respond quickly to emerging pandemic influenza threats.
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 | 0.000 | 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".