Effect of diuretics on renal tubular transport of calcium and magnesium
Bibliographic record
Abstract
Calcium (Ca 2+ ) and Magnesium (Mg 2+ ) reabsorption along the renal tubule is dependent on distinct trans- and paracellular pathways. Our understanding of the molecular machinery involved is increasing. Ca 2+ and Mg 2+ reclamation in kidney is dependent on a diverse array of proteins, which are important for both forming divalent cation-permeable pores and channels, but also for generating the necessary driving forces for Ca 2+ and Mg 2+ transport. Alterations in these molecular constituents can have profound effects on tubular Ca 2+ and Mg 2+ handling. Diuretics are used to treat a large range of clinical conditions, but most commonly for the management of blood pressure and fluid balance. The pharmacological targets of diuretics generally directly facilitate sodium (Na + ) transport, but also indirectly affect renal Ca 2+ and Mg 2+ handling, i.e., by establishing a prerequisite electrochemical gradient. It is therefore not surprising that substantial alterations in divalent cation handling can be observed following diuretic treatment. The effects of diuretics on renal Ca 2+ and Mg 2+ handling are reviewed in the context of the present understanding of basal molecular mechanisms of Ca 2+ and Mg 2+ transport. Acetazolamide, osmotic diuretics, Na + /H + exchanger (NHE3) inhibitors, and antidiabetic Na + /glucose cotransporter type 2 (SGLT) blocking compounds, target the proximal tubule, where paracellular Ca 2+ transport predominates. Loop diuretics and renal outer medullary K + (ROMK) inhibitors block thick ascending limb transport, a segment with significant paracellular Ca 2+ and Mg 2+ transport. Thiazides target the distal convoluted tubule; however, their effect on divalent cation transport is not limited to that segment. Finally, potassium-sparing diuretics, which inhibit electrogenic Na + transport at distal sites, can also affect divalent cation transport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".