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Record W7073765717

Glycogen synthase kinase 3α regulates urine concentrating mechanism in mice

2015· article· en· W7073765717 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicGestational Trophoblastic Disease Studies
Canadian institutionsLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsPolyuriaUrine osmolalityUrineKidneyAquaporin 2Urinary systemVasopressinNephrogenic diabetes insipidus
DOInot available

Abstract

fetched live from OpenAlex

In mammals, glycogen synthase kinase (GSK)3 comprises GSK3α and GSK3β isoforms. GSK3β has been shown to play a role in the ability of kidneys to concentrate urine by regulating vasopressin-mediated water permeability of collecting ducts, whereas the role of GSK3α has yet to be discerned. To investigate the role of GSK3α in urine concentration, we compared GSK3α knockout (GSK3αKO) mice with wild-type (WT) littermates. Under normal conditions, GSK3αKO mice had higher water intake and urine output. GSK3αKO mice also showed reduced urine osmolality and aquaporin-2 levels but higher urinary vasopressin. When water deprived, they failed to concentrate their urine to the same level as WT littermates. The addition of 1-desamino-8-d-arginine vasopressin to isolated inner medullary collecting ducts increased the cAMP response in WT mice, but this response was reduced in GSK3αKO mice, suggesting reduced responsiveness to vasopressin. Gene silencing of GSK3α in mpkCCD cells also reduced forskolin-induced aquaporin-2 expression. When treated with LiCl, an isoform nonselective inhibitor of GSK3 and known inducer of polyuria, WT mice developed significant polyuria within 6 days. However, in GSK3αKO mice, the polyuric response was markedly reduced. This study demonstrates, for the first time, that GSK3α could play a crucial role in renal urine concentration and suggest that GSK3α might be one of the initial targets of Li(+) in LiCl-induced nephrogenic diabetes insipidus.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.280
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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