IL17A genetic variation is associated with altered susceptibility to Gram-positive infection and mortality of severe sepsis
Bibliographic record
Abstract
INTRODUCTION: Interleukin 17A (IL17A) plays a key role in host defense against microbial infection including Gram-positive bacteria. Genetic factors contribute to the host defense, but the role of IL17A single nucleotide polymorphisms (SNPs) has not yet been investigated in severe sepsis. Therefore, we hypothesized that SNPs in the IL17A gene alter susceptibility to infection and clinical outcome of severe sepsis. METHODS: We tested for the association of IL17A SNPs with susceptibility to infection and clinical outcome of severe sepsis using two cohorts of European ancestry (derivation cohort, St Paul's Hospital (SPH), n = 679; validation cohort, Vasopressin and Septic Shock Trial (VASST), n = 517). The primary outcome variable was susceptibility to Gram-positive bacterial infection. The secondary outcome variable was 28-day mortality. RESULTS: Of four tested IL17A tag SNPs (rs4711998, rs8193036, rs2275913, rs1974226), rs1974226 SNP was associated with altered susceptibility to Gram-positive infection in the derivation SPH cohort (corrected P = 0.014). Patients having the rs1974226 GG genotype were more susceptible to Gram-positive infection, compared to AG/AA genotype in the two cohorts of severe sepsis (SPH, P = 0.0036, odds ratio (OR) 2.19, 95% confidence interval (CI) 1.28-3.72; VASST, P = 0.011, OR 1.95, 95%CI 1.16-3.27) and in the subgroup having lung infection (P = 0.017, OR 1.90, 95%CI 1.12-3.21). Furthermore, the IL17A rs1974226 G allele was associated with increased 28-day mortality in two cohorts (SPH, adjusted OR 1.44, 95%CI 1.04-2.02, P = 0.029; VASST, adjusted OR 1.67, 95%CI 1.17-2.40, P = 0.0052). CONCLUSIONS: IL17A genetic variation is associated with altered susceptibility to Gram-positive infection and 28-day mortality of severe sepsis.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".