Exercise‐Induced Changes in Plasma Composition Increase Erythrocyte Na<sup>+</sup>,K<sup>+</sup>‐ATPase, but not Na<sup>+</sup>–K<sup>+</sup>–2Cl<sup>−</sup> Cotransporter, Activity to Stimulate net and Unidirectional K<sup>+</sup> Transport in Humans
Bibliographic record
Abstract
We tested the hypothesis that exercise-induced changes in plasma composition result in peak stimulation of erythrocyte unidirectional K+ (JK,in) and net K+ (JK,net) transport within the first 120 s. In experimental series 1 (7 men; 2 women), plasma [K+] was continuously measured in vitro (37 degrees C) after the addition of red blood cells (RBCs) obtained from rested subjects (resting RBCs) into an exercise-simulated plasma (ESP; increased plasma osmolality, [K+], [H+], [lactate] and [adrenaline] (epinephrine)), and JK,net calculated. In experimental series 2 (7 men; 4 women), resting RBCs were incubated in true exercise plasma (TEP) obtained after two 30 s bouts of high intensity leg cycling exercise to determine JK,net and JK,in (via RBC 86Rb accumulation). JK,net of resting RBCs increased from 0.9 +/- 28.7 in resting plasma to 285 +/- 164 mmol (l RBCs)-1 h-1 in ESP and to 178 +/- 60 mmol (l RBCs)-1 h-1 after 10 s in TEP. Both JK,net and JK,in peaked within 10 s of incubation and decreased rapidly during the initial 120 s. The use of inhibitors for the Na+,K+-ATPase (ouabain) and the Na+-K+-2Cl- cotransporter (NKCC; bumetanide) indicated that rapid increases in JK,in and JK,net upon incubation of resting RBCs in TEP were due primarily to increased Na+,K+-ATPase activity; the NKCC appeared to be involved only when the Na+,K+-ATPase was blocked. It is concluded that RBCs rapidly increase JK,in and JK,net in response to exercise-induced changes in plasma composition.
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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".