Requirements for a high rate of potassium excretion in rats consuming a low electrolyte diet
Bibliographic record
Abstract
Control mechanisms for potassium (K(+)) excretion in humans developed in Palaeolithic times when diets were sodium poor and episodically K(+) rich. Nevertheless, our understanding of the regulation of K(+) excretion comes from experiments in rats with large sodium and K(+) intakes. Our objective was to identify how K(+) excretion was regulated when rats consumed a low NaCl diet to reflect Palaeolithic conditions. Rats that were given mineralocorticoids plus either NaCl, mannitol, or NaHCO(3) had a small kaliuresis. In contrast, KCl load induced a large kaliuresis and a near-maximal luminal [K(+)] in the terminal cortical collecting duct ([K(+)](CCD)). The time course of events was important. The rise in the [K(+)](CCD) was prompt, but the initial kaliuresis was only modest. Over the next 4 h, kaliuresis increased markedly due solely to a higher calculated distal flow rate, which appeared to be due to diminished reabsorption of NaCl in the loop of Henle; of note, the measured papillary [K(+)] rose. In summary, the increase in the [K(+)](CCD) in rats given KCl is likely to be due to an increase in the number of luminal K(+) channels rather than to mechanisms that are known to induce a lumen-negative voltage in cortical distal nephron segments. The higher distal flow rate might be due to a higher interstitial [K(+)], which inhibited NaCl reabsorption in the loop of Henle. Thus, to understand which of the potential control mechanisms are operating, one must look very closely at the conditions imposed by the experimental setting.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".