Kidney stones, hypercalciuria, and recent insights into proximal tubule calcium reabsorption
Bibliographic record
Abstract
PURPOSE OF REVIEW: Most kidney stones are composed of calcium, and the greatest risk factor for kidney stone formation is hypercalciuria. Patients who form kidney stones often have reduced calcium reabsorption from the proximal tubule, and increasing this reabsorption is a goal of some dietary and pharmacological treatment strategies to prevent kidney stone recurrence. However, until recently, little was known about the molecular mechanism that mediates calcium reabsorption from the proximal tubule. This review summarizes newly uncovered key insights and discusses how they may inform the treatment of kidney stone formers. RECENT FINDINGS: Studies examining claudin-2 and claudin-12 single and double knockout mice, combined with cell culture models, support complementary independent roles for these tight junction proteins in contributing paracellular calcium permeability to the proximal tubule. Moreover, a family with a coding variation in claudin-2 causing hypercalciuria and kidney stones have been reported, and reanalysis of Genome Wide Association Study (GWAS) data demonstrates an association between noncoding variations in CLDN2 and kidney stone formation. SUMMARY: The current work begins to delineate the molecular mechanisms whereby calcium is reabsorbed from the proximal tubule and suggests a role for altered claudin-2 mediated calcium reabsorption in the pathogenesis of hypercalciuria and kidney stone formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".