Experimental necrotizing enterocolitis induces neuroinflammation in the neonatal brain
Bibliographic record
Abstract
BACKGROUND: Necrotizing enterocolitis (NEC) is an inflammatory gastrointestinal disease primarily affecting preterm neonates. Neonates with NEC suffer from a degree of neurodevelopmental delay that is not explained by prematurity alone. There is a need to understand the pathogenesis of neurodevelopmental delay in NEC. In this study, we assessed the macroscopic and microscopic changes that occur to brain cell populations in specific brain regions in a neonatal mouse model of NEC. Moreover, we investigated the role of intestinal inflammation as part of the mechanism responsible for the changes observed in the brain of pups with NEC. METHODS: Brains of mice were assessed for gross morphology and cerebral cortex thickness (using histology). Markers for mature neurons, oligodendrocytes, neural progenitor cells, microglia, and astrocytes were used to quantify their cell populations in different regions of the brain. Levels of cell apoptosis in the brain were measured by Western blotting and immunohistochemistry. Endoplasmic reticulum (ER) stress markers and levels of pro-inflammatory cytokines (in the ileum and brain) were measured by RT-qPCR and Western blotting. A Pearson test was used to correlate the levels of cytokines (ELISA) in the brain and ileum and to correlate activated microglia and astrocyte populations to the severity of NEC. RESULTS: NEC pups had smaller brain weights, higher brain-to-body weight ratios, and thinner cortices compared to control pups. NEC pups had increased levels of apoptosis and ER stress. In addition, NEC was associated with a reduction in the number of neurons, oligodendrocytes, and neural progenitors in specific regions of the brain. Levels of pro-inflammatory cytokines and the density of activated microglia and astrocytes were increased in the brain and positively correlated with the increase in the levels pro-inflammatory cytokines in the gut and the severity of NEC damage respectively. CONCLUSIONS: NEC is associated with severe changes in brain morphology, a pro-inflammatory response in the brain that alters cell homeostasis and density of brain cell populations in specific cerebral regions. We show that the severity of neuroinflammation is associated with the severity of NEC. Our findings suggest that early intervention during NEC may reduce the chance of acute neuroinflammation and cerebral damage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".