Novel polymorphism of interleukin-18 associated with greater inflammation after cardiac surgery
Bibliographic record
Abstract
INTRODUCTION: Interleukin (IL)-18 is a key modulator of the cytokine response that leads to organ dysfunction and prolonged intensive care unit (ICU) stay after cardiopulmonary bypass surgery. We hypothesised that variation in the pro-inflammatory gene IL-18 is associated with adverse clinical outcome because of a more intense inflammatory response. METHODS: Haplotypes of the IL-18 gene were inferred from genotypes of 23 Coriell Registry subjects. Four haplotype tag single nucleotide polymorphisms (-607 C/A, -137 G/C, 8148 C/T and 9545 T/G) identified four major haplotype clades. These polymorphisms were genotyped in 658 Caucasian patients undergoing cardiopulmonary bypass surgery. Clinical phenotypes were collected by retrospective chart review. RESULTS: Patients homozygous for the T allele of the 9545 T/G polymorphism had an increased occurrence of prolonged ICU stay (6.8% for TT genotype versus 2.7% for GG or GT genotype; p = 0.015). Patients homozygous for the T allele also had increased occurrence of low systemic vascular resistance index (62%) compared with the GG and GT genotypes (53%; p = 0.045). Patients homozygous for the T allele had increased serum IL-18 concentrations 24 hours post-surgery (p = 0.018), increased pro-inflammatory tumour necrosis factor alpha concentrations (p = 0.014) and decreased anti-inflammatory serum IL-10 concentrations (p = 0.018) 24 hours post-surgery. CONCLUSIONS: The TT genotype of the IL-18 9545 T/G polymorphism is associated with an increased occurrence of prolonged ICU stay post-surgery and greater post-surgical inflammation. These results may be explained by greater serum IL-18, leading to greater pro-versus anti-inflammatory cytokine expression.
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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 | 0.001 |
| 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) | 0.002 | 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".