Sources of axonal calcium loading during in vitro ischemia of rat dorsal roots
Bibliographic record
Abstract
A detailed understanding of injury mechanisms in peripheral nerve fibers will help guide successful design of therapies for peripheral neuropathies. This study was therefore undertaken to examine the ionic mechanisms of Ca2+ overload in peripheral myelinated fibers subjected to chemical inhibition of energy metabolism. Myelinated axons from rat dorsal roots were co-loaded with Ca2+-sensitive (Oregon Green BAPTA-1) and Ca2+-insensitive (Alexa Fluor 594) dextran-conjugated fluorophores and imaged using confocal laser scanning microscopy. Axoplasmic regions were clearly outlined by the Ca2+-insensitive dye, from which axonal Ca2+-dependent fluorescence changes (FCa.ax) were measured. Block of Na+-K+ ATPase (ouabain), opening of Na+ channels (veratridine), and inhibiting energy metabolism (iodoacetate + NaN3) caused a rapid rise in FCa.ax to 96% above control after 30 min. Chemical ischemia (iodoacetate + NaN3) caused a more gradual increase in FCa.ax (54%), which was almost completely dependent on bath Ca2+, indicating that most of the Ca2+ accumulation occurred via influx across the axolemma. Na+ channel block (tetrodotoxin) reduced ischemic FCa.ax rise (14%); however, inhibition of L-type Ca2+ channels (nimodipine) had no effect (60%). In contrast, Na+-Ca2+ exchange inhibition (KB-R7943) significantly reduced ischemic FCa.ax rise (18%). Together our results indicate that the bulk of Ca2+ overload in injured peripheral myelinated axons occurs via reverse Na+-Ca2+ exchange, driven by axonal Na+ accumulation through voltage-gated tetrodotoxin-sensitive Na+ channels. This mechanism may represent a viable therapeutic target for peripheral neuropathies.
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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".