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Record W2558608270 · doi:10.1074/jbc.m116.764720

Correctors and Potentiators Rescue Function of the Truncated W1282X-Cystic Fibrosis Transmembrane Regulator (CFTR) Translation Product

2016· article· en· W2558608270 on OpenAlexafffund
Peter M. Haggie, Puay‐Wah Phuan, Joseph‐Anthony Tan, Haijin Xu, Radu G. Avramescu, Doranda Perdomo, Lorna Zlock, Dennis W. Nielson, Walter E. Finkbeiner, Gergely L. Lukács, A. S. Verkman

Bibliographic record

VenueJournal of Biological Chemistry · 2016
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsMcGill University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Institute of Biomedical Imaging and BioengineeringNational Eye InstituteCanadian Institutes of Health ResearchCystic Fibrosis Foundation TherapeuticsNational Cancer InstituteNational Institutes of HealthCystic Fibrosis Foundation
KeywordsPotentiatorCystic fibrosisIvacaftorRegulatorCystic fibrosis transmembrane conductance regulatorTranslation (biology)Function (biology)AmbroxolMedicineChemistryBiologyCell biologyBiochemistryAnatomyPharmacologyInternal medicineMessenger RNAGene

Abstract

fetched live from OpenAlex

channel activity to that of wild type CFTR. Corrector and potentiator combinations were tested in primary cultures and conditionally reprogrammed cells generated from nasal brushings from one W1282X homozygous subject. Although robust chloride conductance was seen with correctors and potentiators in homozygous ΔF508 cells, increased chloride conductance was not found in W1282X cells despite the presence of adequate transcript levels. Notwithstanding the negative data in W1282X cells from one human subject, we speculate that corrector and potentiator combinations may have therapeutic efficacy in cystic fibrosis caused by the W1282X mutation, although additional studies are needed on human cells from W1282X subjects.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.257
Teacher spread0.239 · 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

Citations92
Published2016
Admission routes2
Has abstractyes

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