Fetal cholinergic anti-inflammatory pathway and necrotizing enterocolitis: the brain-gut connection begins in utero
Bibliographic record
Abstract
Necrotizing enterocolitis (NEC) is an acute neonatal inflammatory disease that affects the intestine and may result in necrosis, systemic sepsis and multisystem organ failure. NEC affects 5-10% of all infants with birth weight ≤ 1500 g or gestational age less than 30 weeks. Chorioamnionitis (CA) is the main manifestation of pathological inflammation in the fetus and is strong associated with NEC. CA affects 20% of full-term pregnancies and upto 60% of preterm pregnancies and, notably, is often an occult finding. Intrauterine exposure to inflammatory stimuli may switch innate immunity cells such as macrophages to a reactive phenotype ("priming"). Confronted with renewed inflammatory stimuli during labour or postnatally, such sensitized cells can sustain a chronic or exaggerated production of proinflammatory cytokines associated with NEC (two-hit hypothesis). Via the cholinergic anti-inflammatory pathway, a neurally mediated innate anti-inflammatory mechanism, higher levels of vagal activity are associated with lower systemic levels of proinflammatory cytokines. This effect is mediated by the α7 subunit nicotinic acetylcholine receptor (α7nAChR) on macrophages. The gut is the most extensive organ innervated by the vagus nerve; it is also the primary site of innate immunity in the newborn. Here we review the mechanisms of possible neuroimmunological brain-gut interactions involved in the induction and control of antenatal intestinal inflammatory response and priming. We propose a neuroimmunological framework to (1) study the long-term effects of perinatal intestinal response to infection and (2) to uncover new targets for preventive and therapeutic intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".