The Role of Polymorphisms in Host Immune Genes in Determining the Severity of Respiratory Illness Caused by Pandemic H1N1 Influenza
Bibliographic record
Abstract
Following the influenza epidemics, it has become clear that severity of illness is not uniform. Comorbidities and immunocompromise account for only a fraction of severe cases and do not explain the differential severity among the otherwise healthy during pandemics. During the 2009 H1N1 pandemic, a focus has been placed on better understanding of the determinants and pathogenesis of severe influenza infections. Much of the current literature has focused on viral genetics and its impact on host immunity as well as novel risk factors for severe infection (particularly within the H1N1 pandemic). The improved understanding of the cellular mechanisms and pathways involved in the pathogenesis of severe disease along with technological advances have allowed a more systematic approach to the exploration of the host genetic determinants of susceptibility and severe respiratory illness. By better defining the role of genetic variability in the immune responses to influenza, and identifying key polymorphisms that either protect against severe manifestation or those that impair the host immune response, it is possible to envision better methods to identify at-risk populations and new targets for therapeutic interventions and vaccines. This review will summarize the accumulated literature examining the immunogenetic factors associated with propensity for the development of severe pandemic H1N1 (pH1N1) manifestations. We will focus on novel and key insights gained through study of ethnic populations that appeared more vulnerable to severe disease during the 2009 H1N1 pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".