Selective cerebral perfusion prevents abnormalities in glutamate cycling and neuronal apoptosis in a model of infant deep hypothermic circulatory arrest and reperfusion
Bibliographic record
Abstract
Deep hypothermic circulatory arrest is often required for the repair of complex congenital cardiac defects in infants. However, deep hypothermic circulatory arrest induces neuroapoptosis associated with later development of neurocognitive abnormalities. Selective cerebral perfusion theoretically provides superior neural protection possibly through modifications in cerebral substrate oxidation and closely integrated glutamate cycling. We tested the hypothesis that selective cerebral perfusion modulates glucose utilization, and ameliorates abnormalities in glutamate flux, which occur in association with neuroapoptosis during deep hypothermic circulatory arrest. Eighteen infant male Yorkshire piglets were assigned randomly to two groups of seven (deep hypothermic circulatory arrest or deep hypothermic circulatory arrest with selective cerebral perfusion for 60 minutes at 18℃) and four control pigs without cardiopulmonary bypass support. Carbon-13-labeled glucose as a metabolic tracer was infused, and gas chromatography-mass spectrometry and nuclear magnetic resonance were used for metabolic analysis in the frontal cortex. Following 2.5 h of cerebral reperfusion, we observed similar cerebral adenosine triphosphate levels, absolute levels of lactate and citric acid cycle intermediates, and carbon-13 enrichment among three groups. However, deep hypothermic circulatory arrest induced significant abnormalities in glutamate cycling resulting in reduced glutamate/glutamine and elevated γ-aminobutyric acid/glutamate along with neuroapoptosis, which were all prevented by selective cerebral perfusion. The data suggest that selective cerebral perfusion prevents these modifications in glutamate/glutamine/γ-aminobutyric acid cycling and protects the cerebral cortex from apoptosis.
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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 | 0.000 |
| 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 | 0.001 |
| 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".