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Record W3002060091 · doi:10.1152/ajprenal.00453.2019

Heterotrimeric Gα<sub>12/13</sub>proteins in kidney injury and disease

2020· review· en· W3002060091 on OpenAlexafffund
Elena Tutunea-Fatan, Jasper C. Lee, Bradley M. Denker, Lakshman Gunaratnam

Bibliographic record

VenueAmerican Journal of Physiology-Renal Physiology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Kinase Regulation and GTPase Signaling
Canadian institutionsWestern UniversityLawson Health Research Institute
FundersCanadian Institutes of Health ResearchKidney Foundation of CanadaGovernment of Canada
KeywordsHeterotrimeric G proteinG proteinEffectorSignal transductionKidneyBiologyPathogenesisG protein-coupled receptorKidney developmentCell biologyComputational biologyBioinformaticsMedicineNeuroscienceGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

Gα 12 and Gα 13 are ubiquitous members of the heterotrimeric guanine nucleotide-binding protein (G protein) family that play central and integrative roles in the regulation of signal transduction cascades within various cell types in the kidney. Gα 12 /Gα 13 proteins enable the kidney to adapt to an ever-changing environment by transducing stimuli from cell surface receptors and accessory proteins to effector systems. Therefore, perturbations in Gα 12 /Gα 13 levels or their activity can contribute to the pathogenesis of various renal diseases, including renal cancer. This review will highlight and discuss the complex and expanding roles of Gα 12 /Gα 13 proteins on distinct renal pathologies, with emphasis on more recently reported findings. Deciphering how the different Gα 12 /Gα 13 interaction networks participate in the onset and development of renal diseases may lead to the discovery of new therapeutic strategies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Physiology-Renal PhysiologySame topicProtein Kinase Regulation and GTPase SignalingFrench-language works237,207