AMPK activation with AICAR provokes an acute fall in plasma [K<sup>+</sup>]
Bibliographic record
Abstract
AMP-activated protein kinase (AMPK), activated by an increase in intracellular AMP-to-ATP ratio, stimulates pathways that can restore ATP levels. We tested the hypothesis that AMPK activation influences extracellular fluid (ECF) K(+) homeostasis. In conscious rats, AMPK was activated with 5-aminoimidazole-4-carboxamide-1-beta-d-ribofuranoside (AICAR) infusion: 38.4 mg x kg bolus then 4 mg x kg(-1) x min(-1) infusion. Plasma [K(+)] and [glucose] both dropped at 1 h of AICAR infusion and [K(+)] dropped to 3.3 +/- 0.04 mM by 3 h, linearly related to the increase in muscle AMPK phosphorylation. AICAR treatment did not increase urinary K(+) excretion. AICAR lowered [K(+)] whether plasma [K(+)] was chronically elevated or lowered. The K(+) infusion rate needed to maintain baseline plasma [K(+)] reached 15.7 +/- 1.3 micromol K(+) x kg(-1) x min(-1) between 120 and 180 min AICAR infusion. In mice expressing a dominant inhibitory form of AMPK in the muscle (Tg-KD1), baseline [K(+)] was not different from controls (4.2 +/- 0.1 mM), but the fall in plasma [K(+)] in response to AICAR (0.25 g/kg) was blunted: [K(+)] fell to 3.6 +/- 0.1 in controls and to 3.9 +/- 0.1 mM in Tg-KD1, suggesting that ECF K(+) redistributes, at least in part, to muscle ICF. In summary, these findings illustrate that activation of AMPK activity with AICAR provokes a significant fall in plasma [K(+)] and suggest a novel mechanism for redistributing K(+) from ECF to ICF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".