Gene expression profiling in necrotizing enterocolitis reveals pathways common to those reported in Crohn’s disease
Bibliographic record
Abstract
BACKGROUND: Necrotizing enterocolitis (NEC) is the most frequent life-threatening gastrointestinal disease experienced by premature infants in neonatal intensive care units. The challenge for neonatologists is to detect early clinical manifestations of NEC. One strategy would be to identify specific markers that could be used as early diagnostic tools to identify preterm infants most at risk of developing NEC or in the event of a diagnostic dilemma of suspected disease. As a first step in this direction, we sought to determine the specific gene expression profile of NEC. METHODS: Deep sequencing (RNA-Seq) was used to establish the gene expression profiles in ileal samples obtained from preterm infants diagnosed with NEC and non-NEC conditions. Data were analyzed with Ingenuity Pathway Analysis and ToppCluster softwares. RESULTS: Data analysis indicated that the most significant functional pathways over-represented in NEC neonates were associated with immune functions, such as altered T and B cell signaling, B cell development, and the role of pattern recognition receptors for bacteria and viruses. Among the genes that were strongly modulated in neonates with NEC, we observed a significant degree of similarity when compared with those reported in Crohn's disease, a chronic inflammatory bowel disease. CONCLUSIONS: Gene expression profile analysis revealed a predominantly altered immune response in the intestine of NEC neonates. Moreover, comparative analysis between NEC and Crohn's disease gene expression repertoires revealed a surprisingly high degree of similarity between these two conditions suggesting a new avenue for identifying NEC biomarkers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".