Interleukin-1 Receptor–Associated Kinase 3 Gene Associates with Susceptibility to Acute Lung Injury
Bibliographic record
Abstract
Sepsis is the most common cause of acute lung injury (ALI), leading to organ dysfunction and death in critically ill patients. Previous studies associated variants of interleukin-1 receptor-associated kinase genes (IRAKs) with differential immune responses to pathogens and with outcomes during sepsis, and revealed that increased expression levels of the IRAK3 gene were correlated with poor outcomes during sepsis. Here we explored whether common variants of the IRAK3 gene were associated with susceptibility to, and outcomes of, severe sepsis. After our discovery of polymorphism, we genotyped a subset of seven single-nucleotide polymorphisms (SNPs) in 336 population-based control subjects and 214 patients with severe sepsis, collected as part of a prospective study of adults from a Spanish network of intensive care units. Whereas IRAK3 SNPs were not associated with susceptibility to severe sepsis, rs10506481 showed a significant association with the development of ALI among patients with sepsis (P = 0.007). The association remained significant after adjusting for multiple comparisons, population stratification, and clinical variables (odds ratio, 2.50; 95% confidence interval, 1.15-5.47; P = 0.021). By imputation, we revealed three additional SNPs independently associated with ALI (P < 0.01). One of these (rs1732887) predicted the disruption of a putative human-mouse conserved transcription factor binding site, and demonstrated functional effects in vitro (P = 0.017). Despite the need for replication in independent studies, our data suggest that common SNPs in the IRAK3 gene may be determinants of sepsis-induced ALI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".