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Record W2153419854 · doi:10.1677/jme-08-0164

Calcium-sensing receptor mutations and denaturing high performance liquid chromatography

2009· article· en· W2153419854 on OpenAlexafffund
David E.C. Cole, Francisco Yun, Betty Wong, Andrew Y. Shuen, Ronald A. Booth, Alfredo Scillitani, Svetlana Pidasheva, Xiang Zhou, Lucie Canaff, Geoffrey N. Hendy

Bibliographic record

VenueJournal of Molecular Endocrinology · 2009
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsMcGill UniversityRoyal Victoria HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMinistero della SaluteKidney Foundation of CanadaDairy Farmers of Canada
KeywordsDenaturing high performance liquid chromatographyAmpliconMissense mutationCalcium-sensing receptorExonMutationHigh Resolution MeltMolecular biologyBiologyGeneticsGeneInternal medicineCalcium metabolismPolymerase chain reactionCalciumMedicine

Abstract

fetched live from OpenAlex

The calcium-sensing receptor (CASR), a plasma membrane G-protein-coupled receptor, is expressed in parathyroid gland and kidney, and controls systemic calcium homeostasis. Inactivating CASR mutations are associated with familial hypocalciuric hypercalcemia (FHH) and neonatal severe hyperparathyroidism, and activating mutations cause autosomal dominant hypocalcemia (ADH). CASR mutation identification plays an important role in the clinical management of mineral metabolism disorders. We describe here a high-throughput method using screening with denaturing high performance liquid chromatography (DHPLC) to initially interrogate 12 amplicons covering translated exons and exon/intron boundaries, followed by sequencing of any amplicon with a modified melting curve relative to wild type, and direct sequencing of a 13th amplicon encoding the COOH-terminal tail to distinguish causative mutations from three common missense single nucleotide polymorphisms. A blinded analysis of 32 positive controls representing mutations throughout the CASR sequence, as well as 22 negative controls, yielded a concordance rate of 100%. We report eight novel and five recurrent FHH mutations, along with six novel and two recurrent ADH mutations. Thus, DHPLC provides a rapid and effective means to screen for CASR mutations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.010
GPT teacher head0.270
Teacher spread0.260 · 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 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
Published2009
Admission routes2
Has abstractyes

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