Back to the Future for Influenza Preimmunity—Looking Back at Influenza Virus History to Infer the Outcome of Future Infections
Bibliographic record
Abstract
The influenza virus-host interaction is a classic arms race. The recurrent and evolving nature of the influenza virus family allows a single host to be infected several times. Locked in co-evolution, recurrent influenza virus infection elicits continual refinement of the host immune system. Here we give historical context of circulating influenza viruses to understand how the individual immune history is mirrored by the history of influenza virus circulation. Original Antigenic Sin was first proposed as the negative influence of the host’s first influenza virus infection on the next and Imprinting modernizes Antigenic Sin incorporating both positive and negative outcomes. Building on imprinting, we refer to preimmunity as the continual refinement of the host immune system with each influenza virus infection. We discuss imprinting and the interplay of influenza virus homology, vaccination, and host age establishing preimmunity. We outline host signatures and outcomes of tandem infection according to the sequence of virus and classify these relationships as monosubtypic homologous, monosubtypic heterologous, heterosubtypic, or heterotypic sequential infections. Finally, the preimmunity knowledge gaps are highlighted for future investigation. Understanding the effects of antigenic variable recurrent influenza virus infection on immune refinement will advance vaccination strategies, as well as pandemic preparedness.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".