Important Role of Reverse Na<sup>+</sup>-Ca<sup>2+</sup>Exchange in Spinal Cord White Matter Injury at Physiological Temperature
Bibliographic record
Abstract
Spinal cord injury is a devastating condition in which most of the clinical disability results from dysfunction of white matter tracts. Excessive cellular Ca(2+) accumulation is a common phenomenon after anoxia/ischemia or mechanical trauma to white matter, leading to irreversible injury because of overactivation of multiple Ca(2+)-dependent biochemical pathways. In the present study, we examined the role of Na(+)-Ca(2+) exchange, a ubiquitous Ca(2+) transport mechanism, in anoxic and traumatic injury to rat spinal dorsal columns in vitro. Excised tissue was maintained in a recording chamber at 37 degrees C and injured by exposure to an anoxic atmosphere for 60 min or locally compressed with a force of 2 g for 15 s. Mean compound action potential amplitude recovered to approximately 25% of control after anoxia and to approximately 30% after trauma. Inhibitors of Na(+)-Ca(2+) exchange (50 microM bepridil or 10 microM KB-R7943) improved functional recovery to approximately 60% after anoxia and approximately 70% after traumatic compression. These inhibitors also prevented the increase in calpain-mediated spectrin breakdown products induced by anoxia. We conclude that, at physiological temperature, reverse Na(+)-Ca(2+) exchange plays an important role in cellular Ca(2+) overload and irreversible damage after anoxic and traumatic injury to dorsal column white matter tracts.
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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 | 0.001 | 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".