MétaCan
Menu
Back to cohort
Record W2110651762 · doi:10.1681/asn.2012060582

aPKCλ/ι and aPKCζ Contribute to Podocyte Differentiation and Glomerular Maturation

2013· article· en· W2110651762 on OpenAlexfundno aff
Björn Hartleben, Eugen Widmeier, Martina Suhm, Kirstin Worthmann, Christoph Schell, Martin Helmstädter, Thorsten Wiech, Gerd Walz, Michael Leitges, Mario Schiffer, Tobias B. Huber

Bibliographic record

VenueJournal of the American Society of Nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsPodocyteSlit diaphragmCell biologyGene isoformBiologyKidneyProteinuriaEndocrinologyGeneticsGene

Abstract

fetched live from OpenAlex

Precise positioning of the highly complex interdigitating podocyte foot processes is critical to form the normal glomerular filtration barrier, but the molecular programs driving this process are unknown. The protein atypical protein kinase C (aPKC)--a component of the Par complex, which localizes to tight junctions and interacts with slit diaphragm proteins--may play a role. Here, we found that the combined deletion of the aPKCλ/ι and aPKCζ isoforms in podocytes associated with incorrectly positioned centrosomes and Golgi apparatus and mislocalized molecules of the slit diaphragm. Furthermore, aPKC-deficient podocytes failed to form the normal network of foot processes, leading to defective glomerular maturation with incomplete capillary formation and mesangiolysis. Our results suggest that aPKC isoforms orchestrate the formation of the podocyte processes essential for normal glomerular development and kidney function. Defective aPKC signaling results in a dramatically simplified glomerular architecture, causing severe proteinuria and perinatal death.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicRenal Diseases and GlomerulopathiesFrench-language works237,207