Childhood resolution of early abnormal miRNA following neonatal encephalopathy
Bibliographic record
Abstract
Neonatal Encephalopathy (NE) is a clinical syndrome presenting as neurological dysfunction. Persistent dysregulated Inflammation is associated with NE and miRNA biomarkers may correlate with developmental outcomes. We investigated miR-20a, miR-20b, miR-93 and miR-532 expression at birth and childhood following Neonatal Encephalopathy (NE) as potential biomarkers of inflammation, brain development, and long-term outcomes. Blood samples were collected from neonates with NE (week 1 of life) and children post-NE (2–5 years of age) and compared to age-matched controls (Neonatal and paediatric). Whole blood was stimulated ex-vivo with lipopolysaccharide, and total RNA was extracted from serum. MiR-20a, miR-20b, miR-93 and miR-532 were identified by TaqMan ® Advanced miRNA Assays. Forty-five children were recruited ( n = 11–12 in each group). MiR-20b, miR-93 and miR-532 significantly increased in neonates with NE compared to neonatal controls. MiR-20b expression decreased in children with NE compared to neonates with NE, to childhood control levels. MiR-93 increased in control children compared to control neonatal. MiR-20b significantly decreased with lipopolysaccharide in children with NE compared to their paired neonatal NE sample. MiR-20b, miR-93, and miR-532-5p are linked to neuron development and cellular stress responses. Their dysregulated expression might indicate altered immune responses and have potential as a biomarker.
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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".