Implication of K<sub>ir</sub>4.1 Channel in Excess Potassium Clearance: An<i>In Vivo</i>Study on Anesthetized Glial-Conditional K<sub>ir</sub>4.1 Knock-Out Mice
Bibliographic record
Abstract
The K(ir)4.1 channel is crucial for the maintenance of the resting membrane potential of glial cells, and it is believed to play a main role in the homeostasis of extracellular potassium. To understand its importance in these two phenomena, we have measured in vivo the variations of extracellular potassium concentration ([K(+)](o)) (with potassium-sensitive microelectrodes) and membrane potential of glial cells (with sharp electrodes) during stimulations in wild-type (WT) mice and glial-conditional knock-out (cKO) K(ir)4.1 mice. The conditional knockout was driven by the human glial fibrillary acidic protein promoter, gfa2. Experiments were performed in the hippocampus of anesthetized mice (postnatal days 17-24). Low level stimulation (<20 stimuli, 10 Hz) induced a moderated increase of [K(+)](o) (<2 mm increase) in both WT and cKO mice. However, cKO mice exhibited slower recovery of [K(+)](o) levels. With long-lasting stimulation (300 stimuli, 10 Hz), [K(+)](o) in WT and cKO mice displayed characteristic ceiling level (>2 mm increase) and recovery undershoot, with a more pronounced and prolonged undershoot in cKO mice. In addition, cKO glial cells were more depolarized, and, in contrast to those from WT mice, their membrane potential did not follow the stimulation-induced [K(+)](o) changes, reflecting the loss of their high potassium permeability. Our in vivo results support the role of K(ir)4.1 in setting the membrane potential of glial cells and its contribution to the glial potassium permeability. In addition, our data confirm the necessity of the K(ir)4.1 channel for an efficient uptake of K(+) by glial cells.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".