An integrative, in situ approach to examining K<sup>+</sup> flux in resting skeletal muscle
Bibliographic record
Abstract
The contributions of Na+/K+-ATPase, K+ channels, and the NaK2Cl cotransporter (NKCC) to total and unidirectional K+ flux were determined in mammalian skeletal muscle at rest. Rat hindlimbs were perfused in situ via the femoral artery with a bovine erythrocyte perfusion medium that contained either 86Rb or 42K, or both simultaneously, to determine differences in ability to trace unidirectional K+ flux in the absence and presence of K+-flux inhibitors. In most experiments, the unidirectional flux of K+ into skeletal muscle (JinK) measured using 86Rb was 810% lower than JinK measured using 42K. Ouabain (5 mM) was used to inhibit Na+/K+-ATPase activity, 0.06 mM bumetanide to inhibit NKCC activity, 1 mM tetracaine or 0.5 mM barium to block K+ channels, and 0.05 mM glybenclamide (GLY) to block ATP-sensitive K+ (KATP) channels. In controls, JinK remained unchanged at 0.31 ± 0.03 µmol·g1·min1 during 55 min of perfusion. The ouabain-sensitive Na+/K+-ATPase contributed to 50 ± 2% of basal JinK, K+ channels to 47 ± 2%, and the NKCC to 12 ± 1%. GLY had minimal effect on JinK, and both GLY and barium inhibited unidirectional efflux of K+ (JoutK) from the cell through K+ channels. Combined ouabain and tetracaine reduced JinK by 55 ± 2%, while the combination of ouabain, tetracaine, and bumetanide reduced JinK by 67 ± 2%, suggesting that other K+-flux pathways may be recruited because the combined drug effects on inhibiting JinK were not additive. The main conclusions are that the NKCC accounted for about 12% of JinK, and that KATP channels accounted for nearly all of the JoutK, in resting skeletal muscle in situ.Key words: sodium potassium chloride cotransporter, NKCC, Na+/K+-ATPase, potassium channels, potassium transport, in situ rat hindlimb.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".