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Record W4321615129 · doi:10.4103/ijn.ijn_318_21

Cystic diseases of the kidneys: From bench to bedside

2023· review· en· W4321615129 on OpenAlexaff
Rupesh Raina, Francis Lomanta, Siddhartha Singh, Alisha Anand, Riti Kalra, Vignasiddh Enukonda, Oren Barat, Davinder Pandher, Sidharth K. Sethi

Bibliographic record

VenueIndian Journal of Nephrology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCiliumNephronophthisisCystic kidney diseaseMedicinePolycystic kidney diseaseAutosomal Recessive Polycystic Kidney DiseaseCiliopathiesDiseasePathologyTuberous sclerosisBardet–Biedl syndromePKD1KidneyAutosomal dominant polycystic kidney diseaseCiliopathyPhenotypeBioinformaticsGeneticsBiologyInternal medicine

Abstract

fetched live from OpenAlex

Exploration into the causes of hereditary renal cystic diseases demonstrates a deep-rooted connection with the proteomic components of the cellular organelle cilia. Cilia are essential to the signaling cascades, and their dysfunction has been tied to a range of renal cystic diseases initiating with studies on the oak ridge polycystic kidney (ORPK) mouse model. Here, we delve into renal cystic pathologies that have been tied with ciliary proteosome and highlight the genetics associated with each. The pathologies are grouped based on the mode of inheritance, where inherited causes that result in cystic kidney disease phenotypes include autosomal dominant and autosomal recessive polycystic kidney disease, nephronophthisis (Bardet-Biedl syndrome and Joubert Syndrome), and autosomal dominant tubulointerstitial kidney disease. Alternatively, phakomatoses-, also known as neurocutaneous syndromes, associated cystic kidney diseases include tuberous sclerosis (TS) and Von Hippel-Lindau (VHL) disease. Additionally, we group the pathologies by the mode of inheritance to discuss variations in recommendations for genetic testing for biological relatives of a diagnosed individual.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.508
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.297
Teacher spread0.277 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIndian Journal of NephrologySame topicGenetic and Kidney Cyst DiseasesFrench-language works237,207