Effects of the Noradrenergic System in Rat White Matter Exposed to Oxygen–Glucose Deprivation<i>In Vitro</i>
Bibliographic record
Abstract
Norepinephrine (NE) is released in excess into the extracellular space during oxygen-glucose deprivation (OGD) in brain, increasing neuronal metabolism and aggravating glutamate excitoxicity. We used isolated rat optic nerve and spinal cord dorsal columns to determine whether the noradrenergic system influences axonal damage in white matter. Tissue was studied electrophysiologically by recording the compound action potential (CAP) before and after exposure to 60 min of OGD at 36 degrees C. Depleting catecholamine stores with reserpine was protective and improved CAP recovery after 1 h of reperfusion from 17% (control) to 35%. Adding NE during OGD decreased CAP recovery to 8%, and adding NE to reserpine during OGD eliminated the protective effect of the latter. Selective inhibitors of Na(+)-dependent norepinephrine transport desipramine and nisoxetine improved recovery to 58% and 44%, respectively. alpha2 adrenergic receptor agonists UK14,304 and medetomidine improved CAP recovery to 41% and 46% after 1 h of OGD. Curiously, alpha2 antagonists alone were also highly protective (e.g., atipamezole: 86% CAP recovery), at concentrations that did not affect baseline excitability. The protective effect of alpha2 receptor modulation was corroborated by imaging fluorescent Ca(2+) and Na(+) indicators within axons during OGD. Both agonists and antagonists significantly reduced axonal Ca(2+) and Na(+) accumulation in injured axons. These data suggest that the noradrenergic system plays an active role in the pathophysiology of axonal ischemia and that alpha2 receptor modulation may be useful against white matter injury.
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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) | 0.001 | 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".