Effect of diuretics on renal tubular transport of calcium and magnesium
Bibliographic record
Abstract
Calcium (Ca2+) and Magnesium (Mg2+) reabsorption along the renal tubule is dependent on distinct trans- and paracellular pathways. Our understanding of the molecular machinery involved is increasing. Ca2+and Mg2+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 Ca2+and Mg2+transport. Alterations in these molecular constituents can have profound effects on tubular Ca2+and Mg2+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 Ca2+and Mg2+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 Ca2+and Mg2+handling are reviewed in the context of the present understanding of basal molecular mechanisms of Ca2+and Mg2+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 Ca2+transport predominates. Loop diuretics and renal outer medullary K+(ROMK) inhibitors block thick ascending limb transport, a segment with significant paracellular Ca2+and Mg2+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 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".