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Record W2123629905 · doi:10.1681/asn.2005101074

Suppression of Ureteric Bud Apoptosis Rescues Nephron Endowment and Adult Renal Function in Pax2 Mutant Mice

2006· article· en· W2123629905 on OpenAlexaff
Alison Dziarmaga, Michael R. Eccles, Paul Goodyer

Bibliographic record

VenueJournal of the American Society of Nephrology · 2006
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsMontreal Children's Hospital
Fundersnot available
KeywordsNephronUreteric budBiologyKidney developmentEndocrinologyInternal medicineKidneyGlomerulosclerosisMedicineProteinuriaGenetics

Abstract

fetched live from OpenAlex

The molecular mechanisms that set congenital nephron number are unknown. However, humans with modest suboptimal nephron number may be at increased risk for essential hypertension, and those with more severe nephron deficits at birth may develop progressive renal insufficiency. A model of branching morphogenesis during fetal kidney development in which the extent of ureteric bud arborization is dependent on suppression of programmed cell death has been proposed. This study shows that the increased apoptosis and reduced ureteric bud branching of heterozygous Pax2 mutant mice is associated with 40% decrease in nephron number at birth. This leads to postnatal glomerular hypertrophy and long-term renal insufficiency in the absence of glomerulosclerosis. To determine whether restoration of antiapoptotic factors alone is sufficient to rescue the nephron deficit in these mice, a BCL2 transgene that is under the control of the PAX2 promoter was targeted to the ureteric bud. The transgene suppressed programmed cell death in the ureteric bud lineage, increased nephron number to 90% of that of wild-type littermates at birth, and normalized renal function at 1 yr. These observations lend strong support to the hypothesis that factors that control ureteric bud apoptosis are powerful determinants of congenital nephron endowment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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 designObservational
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

Citations68
Published2006
Admission routes1
Has abstractyes

Explore more

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