Membrane trauma and Na<sup>+</sup>leak from Nav1.6 channels
Bibliographic record
Abstract
During brain trauma, white matter experiences shear and stretch forces that, without severing axons, nevertheless trigger their secondary degeneration. In central nervous system (CNS) trauma models, voltage-gated sodium channel (Nav) blockers are neuroprotective. This, plus the rapid tetrodotoxin-sensitive Ca2+ overload of stretch-traumatized axons, points to "leaky" Nav channels as a pivotal early lesion in brain trauma. Direct effects of mechanical trauma on neuronal Nav channels have not, however, been tested. Here, we monitor immediate responses of recombinant neuronal Nav channels to stretch, using patch-clamp and Na+-dye approaches. Trauma constituted either bleb-inducing aspiration of cell-attached oocyte patches or abrupt uniaxial stretch of cells on an extensible substrate. Nav1.6 channel transient current displayed irreversible hyperpolarizing shifts of steady-state inactivation [availability(V)] and of activation [g(V)] and, thus, of window current. Left shift increased progressively with trauma intensity. For moderately intense patch trauma, a approximately 20-mV hyperpolarizing shift was registered. Nav1.6 voltage sensors evidently see lower energy barriers posttrauma, probably because of the different bilayer mechanics of blebbed versus intact membrane. Na+ dye-loaded human embryonic kidney (HEK) cells stably transfected with alphaNav1.6 were subjected to traumatic brain injury-like stretch. Cytoplasmic Na+ levels abruptly increased and the trauma-induced influx had a significant tetrodotoxin-sensitive component. Nav1.6 channel responses to cell and membrane trauma are therefore consistent with the hypothesis that mechanically induced Nav channel leak is a primary lesion in traumatic brain injury. Nav1.6 is the CNS node of Ranvier Nav isoform. When, during head trauma, nodes experienced bleb-inducing membrane damage of varying intensities, nodal Nav1.6 channels should immediately "leak" over a broadly left-smeared window current range.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".