Neocortical in vivo focal and spreading potassium responses and the influence of astrocytic gap junctional coupling
Bibliographic record
Abstract
Raised extracellular potassium ion (K+) concentration is associated with several disorders including migraine, stroke, neurotrauma and epilepsy. K+ spatial buffering is a well-known mechanism for extracellular K+ regulation/distribution. Astrocytic gap junction-mediated buffering is a controversial candidate for K+ spatial buffering. To further investigate the existence of a K+ spatial buffering and to assess the involvement of astrocytic gap junctional coupling in K+ redistribution, we hypothesized that neocortical K+ and concomitant spreading depolarization (SD)-like responses are controlled by powerful local K+ buffering mechanisms and that K+ buffering/redistribution occurs partially through gap junctional coupling. Herein, we show, in vivo, that a threshold amount of focally applied KCl is required to trigger local and/or distal K+ responses, accompanied by a SD-like response. This observation indicates the presence of powerful local K+ buffering which mediates a rapid return of extracellular K+ to the baseline. Application of gap junctional blockers, carbenoxolone and Gap27, partially modulated the amplitude and shape of the K+ response and noticeably decreased the velocity of the spreading K+ and SD-like responses. Opening of gap junctions by trimethylamine, slightly decreased the amplitude of the K+ response and markedly increased the velocity of redistribution of K+ and SD-like events. We conclude that spreading K+ responses reflect powerful local K+ buffering mechanisms which are partially modulated by gap junctional communication. Gap junctional coupling mainly affected the velocity of the K+ and SD-like responses.
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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 | 0.000 | 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".