Cytokine gene polymorphisms in preterm infants with necrotising enterocolitis: genetic association study
Bibliographic record
Abstract
BACKGROUND: The inflammatory cytokine cascade is implicated in the pathogenesis of necrotising enterocolitis (NEC). Genetic association studies of cytokine polymorphisms may help to detect molecular mechanisms that are causally related to the disease process. AIM: To examine associations between the common genetic variants in candidate inflammatory cytokine genes and NEC in preterm infants. METHODS: Multi-centre case-control and genetic association study. DNA samples were collected from 50 preterm infants with NEC and 50 controls matched for gestational age and ethnic group recruited to a multi-centre case-control study. Ten candidate single-nucleotide polymorphisms in cytokines previously associated with infectious or inflammatory diseases were genotyped. The findings were included in random-effects meta-analyses with data from previous genetic association studies. RESULTS: All allele distributions were in Hardy-Weinberg equilibrium. None of the studied cytokine polymorphisms was significantly associated with NEC. Four previous genetic association studies of cytokine polymorphisms and NEC in preterm infants were found. Meta-analyses were possible for several single-nucleotide polymorphisms. These increased the precision of the estimates of effect size but did not reveal any significant associations. CONCLUSIONS: The available data are not consistent with more than modest associations between these candidate cytokine variant alleles and NEC in preterm infants. Data from future association studies of these polymorphisms may be added to the meta-analyses to obtain more precise estimates of effects sizes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".