Renal Collecting Duct Specific GSK 3 alpha Regulates Cellular Distribution and Lithium‐Induced NDI
Bibliographic record
Abstract
Lithium (Li) induced nephrogenic diabetes insipidus is associated with reduced Aquaporin 2 (AQP2) levels and increased proliferation of collecting duct cells (CD) in rodent models. Li is a potent inhibitor of glycogen synthase kinase 3 (GSK3). GSK3 inhibition is known to increase cell proliferation in non‐renal cells. In the current study we examined isoform specific roles of GSK3α and GSK3β in CD structure and function in normal and Li treated mice. We found that gene deletion of GSK3 alpha (3 alpha −/−) caused urinary concentrating defect and polyuria at basal conditions. In these mice, AQP2 protein levels were lower and H‐ATPase, a marker for type A intercalated cells were significantly higher, compared to WT or 3β‐CDnull mice. LiCl treatment (4mmol/Kg, daily IP injection) for 6 days increased urine output and reduced AQP2 levels in WT, but not in 3 alpha −/− mice. Immunostaining for PCNA, marker for dividing cells, AQP4 for principal cells and H‐ATPase demonstrated increase in proliferation of principal cells of cortical and medullary CD cells in WT and 3beta‐CDnull mice, but not in 3 alpha −/− mice. These studies showed that gene deletion of GSK3 alpha changed normal cell distribution in renal CD, reduced AQP2 expression and caused polyuria and that, GSK3 alpha, rather than GSK3beta, is a critical target of Li in renal CDs.
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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) | 0.000 | 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) | 0.001 | 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".