Traumatic Axonal Injury Induces Calcium Influx Modulated by Tetrodotoxin-Sensitive Sodium Channels
Bibliographic record
Abstract
Diffuse axonal injury (DAI) is one of the most common and important pathologies resulting from the mechanical deformation of the brain during trauma. It has been hypothesized that calcium influx into axons plays a major role in the pathophysiology of DAI. However, there is little direct evidence to support this hypothesis, and mechanisms of potential calcium entry have not been explored. In the present study, we used an in vitro model of axonal stretch injury to evaluate the extent and modulation of calcium entry after trauma. Using a calcium-sensitive dye, we observed a dramatic increase in intra-axonal calcium levels immediately after injury. Axonal injury in a calcium-free extracellular solution resulted in no change in calcium concentration, suggesting an extracellular source for the increased post-traumatic calcium levels. We also found that the post-traumatic change in intra-axonal calcium was completely abolished by the application of the sodium channel blocker tetrodotoxin or by replacement of sodium with N-methyl-d-glucamine. In addition, application of the voltage-gated calcium channel (VGCC) blocker omega-conotoxin MVIIC attenuated the post-traumatic increase in calcium. Furthermore, blockade of the Na(+)-Ca(2+) exchanger with bepridil modestly reduced the calcium influx after injury. In contrast to previously proposed mechanisms of calcium entry after axonal trauma, we found no evidence of calcium entry through mechanically produced pores (mechanoporation). Rather, our results suggest that traumatic deformation of axons induces abnormal sodium influx through mechanically sensitive Na(+) channels, which subsequently triggers an increase in intra-axonal calcium via the opening of VGCCs and reversal of the Na(+)-Ca(2+) exchanger.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".