Benefits of hypothermia in neonatal arterial ischemic strokes: A preclinical study
Bibliographic record
Abstract
Abstract Background There is currently no targeted treatment available for neonatal arterial ischemic strokes (NAIS). Epidemiological studies demonstrated that perinatal infection/inflammation, peripartum hypoxia, and occlusion of the internal carotid tree are the main determinants of NAIS. The well‐established benefit of therapeutic hypothermia (HT) in neonatal encephalopathy due to diffuse hypoxia‐ischemia provides a rationale for the potential use of HT as a neuroprotective strategy in NAIS. Methods We used a rat model to reproduce the most prevalent human physiopathological scenario of NAIS. The neuroprotective effect of HT was measured by morphometric magnetic resonance imaging, [18F] fluorodeoxyglucose (FDG) metabolic activity by positron emission tomography/computed tomography, and behavioral tests. Results HT (a) prevented the occurrence of 44% of NAIS, (b) reduced the volume of strokes by 37%, (c) enhanced [18F] FDG metabolic activity within the territory of the occluded carotid artery, and (d) improved motor behavior. Both morphometric and metabolic techniques showed consistently that HT provided a neuroprotective effect located in the motor cortex, hippocampus, and caudate‐putamen. Conclusion Through combining anatomical, metabolic imaging, and behavioral studies, our study provides evidence of neuroprotective effects of HT in NAIS. These results are potentially translational to human NAIS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".