Cytokine dysregulation persists in childhood post Neonatal Encephalopathy
Bibliographic record
Abstract
BACKGROUND: Cytokines are possible mediators of neuroinflammation and associated with adverse outcome in neonatal encephalopathy (NE). Our aim was to explore cytokine response in children with Neonatal Encephalopathy (NE) at school age compared to age-matched controls. METHOD: Follow up at school age, children who had NE and age-matched controls were assessed for their cytokine responses and neurodevelopment outcome. Pro- and anti-inflammatory cytokines in the serum, [Interleukin (IL)-1α, IL-1β, IL-2, IL-6, IL-8, IL-18, Tumor necrosis factor (TNF)-α, TNF β, Interferon (IFN)-γ, granulocyte-macrophage colony-stimulating factor (GM-CSF), vascular endothelial growth factor (VEGF), erythropoietin (EPO), IL-10 & IL-1RA] were measured at baseline and in response to in vitro stimulation with lipopolysaccharide (LPS: endotoxin). RESULTS: GM-CSF, TNF-β, IL-2 IL-6 and IL-8 were significantly elevated at school age following NE (n = 40) compared to controls (n = 37). A rise in GM-CSF, IL-8, TNF-α, IL-1β, & IL-6 were seen in NE group following LPS stimulation. Relative LPS hypo-responsiveness was also noted in children with severe NE with IL-10, VEGF, EPO and TNF-β. Elevated TNF-β was associated with low gross motor scores on assessment at school age. CONCLUSION: School-age children post-NE had significantly altered cytokine responses to endotoxin compared to controls. TNF-β was associated with adverse developmental outcomes. This suggests the inflammatory process may persist into childhood and a longer therapeutic window may be available for neuroprotection therapies.
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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 | 0.000 | 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".