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Record W3111076573 · doi:10.1152/ajprenal.00214.2020

Novel Fanconi renotubular syndromes provide insights in proximal tubule pathophysiology

2020· review· en· W3111076573 on OpenAlexaff
Mathieu Lemaire

Bibliographic record

VenueAmerican Journal of Physiology-Renal Physiology · 2020
Typereview
Languageen
FieldMedicine
TopicBiomedical Research and Pathophysiology
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsPathophysiologyFanconi syndromeMedicineInternal medicineKidney

Abstract

fetched live from OpenAlex

The various forms of Fanconi renotubular syndromes (FRTS) offer significant challenges for clinicians and present unique opportunities for scientists who study proximal tubule physiology. This review will describe the clinical characteristics, genetic underpinnings, and underlying pathophysiology of the major forms of FRST. Although the classic forms of FRTS will be presented (e.g., Dent disease or Lowe syndrome), particular attention will be paid to five of the most recently discovered FRTS subtypes caused by mutations in the genes encoding for L-arginine:glycine amidinotransferase ( GATM), solute carrier family 34 (type Ii sodium/phosphate cotransporter), member 1 ( SLC34A1), enoyl-CoAhydratase/3-hydroxyacyl CoA dehydrogenase ( EHHADH), hepatocyte nuclear factor 4A ( HNF4A), or NADH dehydrogenase complex I, assembly factor 6 ( NDUFAF6). We will explore how mutations in these genes revealed unexpected mechanisms that led to compromised proximal tubule functions. We will also describe the inherent challenges associated with gene discovery studies based on findings derived from small, single-family studies by focusing the story of FRTS type 2 ( SLC34A1). Finally, we will explain how extensive alternative splicing of HNF4A has resulted in confusion with mutation nomenclature for FRTS type 4.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.034
GPT teacher head0.335
Teacher spread0.301 · 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

Citations39
Published2020
Admission routes1
Has abstractyes

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