A Potential Role for the <b><i>NOD1</i></b> Variant <b><i>(</i></b>rs6958571) in Gram-Positive Blood Stream Infection in ELBW Infants
Bibliographic record
Abstract
BACKGROUND: The genetic basis of sepsis susceptibility in preterm infants remains understudied. Herein, we investigated the nucleotide binding-oligomerization domain (NOD)-like receptor (NLR) family of immune receptors as putative loci for preterm sepsis susceptibility. OBJECTIVE: To determine whether single nucleotide polymorphisms (SNPs) in NLR genes are associated with blood stream infections (BSI) in premature infants. METHODS: An international cohort of infants with gestational age (GA) <35 weeks were genotyped for SNPs in the ATG16L1, CARD8, NLRP3, NOD2, and NOD1 genes. χ2 and logistic regression analyses were used to examine relationships between NLR variants and BSI. RESULTS: Among 764 infants, 138 developed BSI, 113 had gram-positive bacterial (GPB) BSI, and 28 had gram-negative bacterial (GNB) BSI. Infants with BSI had a lower birth weight and GA (p < 0.001), but did not differ in gender, race, or chorioamnionitis. NLR variants were not associated with GPB or GNB BSI in the entire cohort. The CC genotype of the NOD1 SNP (rs6958571) was associated with increased GPB BSI in extremely low birth weight (ELBW, birth weight <1,000 g) infants (OR = 3.3, 95% CI: 1.4-7.5, p = 0.003, n = 362) and in Caucasian infants (OR = 2.5, 95% CI: 1.2-5.4, p = 0.016, n = 535). Regression models adjusting for clinical variables identified ELBW status and the NOD1 CC genotype as risk factors for GPB BSI in Caucasian infants. CONCLUSIONS: In this study investigating relationships between NLR variants and sepsis in infants with GA <35 weeks, the NOD1 (rs6958571) SNP was associated with GPB BSI in Caucasian infants and ELBW infants. Replication of our results in an independent cohort would support a role for NLR variants in determining sepsis risk in ELBW infants.
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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 | 0.001 | 0.001 |
| 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".