Physiological roles and molecular mechanisms of K<sup>+</sup>‐Cl<sup>−</sup> cotransport in the mammalian kidney and cardiovascular system: where are we?
Bibliographic record
Abstract
Abstract In the early 80s, renal microperfusion studies led to the identification of a basolateral K+‐Cl− cotransport mechanism in the proximal tubule, thick ascending limb of Henle and collecting duct. More than ten years later, this mechanism was found to be accounted for by three different K+‐Cl− cotransporters (KCC1, KCC3 and KCC4) that are differentially distributed along the renal epithelium. Two of these isoforms (KCC1 and KCC3) were also found to be expressed in arterial walls, the myocardium and a variety of neurons. Subsequently, valuable insights have been gained into the molecular and physiological properties of the KCCs in both the mammalian kidney and cardiovascular system. There is now robust evidence indicating that KCC4 sustains distal renal acidification and that KCC3 regulates myogenic tone in resistance vessels. However, progress in understanding the functional significance of these transporters has been slow, probably because each of the KCC isoforms is not identically distributed among species and some of them share common subcellular localizations with other KCC isoforms or sizeable conductive Cl− pathways. In addition, the mechanisms underlying the process of K+‐Cl− cotransport are still ill defined. The present review focuses on the knowledge gained regarding the roles and properties of KCCs in renal and cardiovascular tissues.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".