A host-based whole genome sequencing study reveals novel risk loci associated with severity of influenza A(H1N1)pdm09 infection
Bibliographic record
Abstract
Influenza A(H1N1)pdm09 virus has remained in a seasonal circulation since being recognized in 2009. Although it followed a mild course in most patients, in others it caused a series of severe clinical illnesses. Epidemiologic studies have implicated that host factors have a major influence on the disease severity of influenza A(H1N1)pdm09 infection. However, an understanding of relevant genetic variations and the underlying mechanisms is still limited. In this present study, we used a host-based whole genome sequencing (WGS) method to comprehensively explore the genetic risk loci associated with severity of influenza A(H1N1)pdm09 infection. From the common single-nucleotide variants (SNVs) analysis, we identified the abnormal nominally significant (P < 1 × 10−4) common SNVs enriched in PTBP3 gene. The results of rare functional SNVs analysis supported that there were several novel candidate genes might confer risk of severe influenza A(H1N1)pdm09 diseases, such as FTSJ3, CPVL, BST2, NOD2 and MAVS. Moreover, our results of gene set based analysis indicated that the HIF-1 transcription factor and IFN-γ pathway might play an important role in the underlying mechanism of severe influenza A(H1N1)pdm09. These findings will increase our knowledge about biological mechanism underlying the severe influenza A(H1N1)pdm09 and facilitate to design novel personalized treatments.
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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".