An Acute Infusion of Lactic Acid Lowers the Concentration of Potassium in Arterial Plasma by Inducing a Shift of Potassium into Cells of the Liver in Fed Rats
Bibliographic record
Abstract
BACKGROUND: Potassium (K(+)) input occurs after meals or during ischemic exercise and is accompanied by a high concentration of L-lactate in plasma (P(L-lactate)). METHODS: We examined whether infusing 100 μmol L-lactic acid/min for 15 min would lead to a fall in the arterial plasma K(+) concentration (P(K)). We also aimed to evaluate the mechanisms involved in normal rats compared with rats with acute hyperkalemia caused by a shift of K(+) from cells or a positive K(+) balance. RESULTS: There was a significant fall in P(K) in normal rats (0.25 mM) and a larger fall in P(K) in both models of acute hyperkalemia (0.6 mM) when the P(L-lactate) rose. The arterial P(K) increased by 0.8 mM (p < 0.05) 7 min after stopping this infusion despite a 2-fold rise in the concentration of insulin in arterial plasma (P(Insulin)). There was a significant uptake of K(+) by the liver, but not by skeletal muscle. In rats pretreated with somatostatin, P(Insulin) was low and infusing L-lactic acid failed to lower the P(K). CONCLUSIONS: A rise in the P(L-lactate) in portal venous blood led to a fall in the P(K) and insulin was permissive. Absorption of glucose by the Na(+)-linked glucose transporter permits enterocytes to produce enough ADP to augment aerobic glycolysis, raising the P(L-lactate) in the portal vein to prevent postprandial hyperkalemia.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".