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Record W2407212762 · doi:10.1038/mtm.2015.34

Testing gene therapy vectors in human primary nasal epithelial cultures

2015· article· en· W2407212762 on OpenAlexafffund
Huibi Cao, Hong Ouyang, Wan Ip, Kai Du, Wenming Duan, Julie Avolio, Jing Wu, Cathleen Duan, Herman Yeger, Christine E. Bear, Tanja Gonska, Jim Hu

Bibliographic record

VenueMolecular Therapy — Methods & Clinical Development · 2015
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsCystic fibrosisCystic fibrosis transmembrane conductance regulatorGenetic enhancementTransduction (biophysics)Nasal polypsMedicineMucous membrane of noseChloride channelApical membraneViral vectorImmunologyCancer researchGenePathologyEpitheliumCell biologyBiologyInternal medicineRecombinant DNAGeneticsBiochemistry

Abstract

fetched live from OpenAlex

Cystic fibrosis (CF) results from mutations in the CF transmembrane conductance regulator (CFTR) gene, which codes for a chloride/bicarbonate channel in the apical epithelial membranes. CFTR dysfunction results in a multisystem disease including the development of life limiting lung disease. The possibility of a cure for CF by replacing defective CFTR has led to different approaches for CF gene therapy; all of which ultimately have to be tested in preclinical model systems. Primary human nasal epithelial cultures (HNECs) derived from nasal turbinate brushing were used to test the efficiency of a helper-dependent adenoviral (HD-Ad) vector expressing CFTR. HD-Ad-CFTR transduction resulted in functional expression of CFTR at the apical membrane in nasal epithelial cells obtained from CF patients. These results suggest that HNECs can be used for preclinical testing of gene therapy vectors in CF.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.205
GPT teacher head0.516
Teacher spread0.311 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations27
Published2015
Admission routes2
Has abstractyes

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Same venueMolecular Therapy — Methods & Clinical DevelopmentSame topicCystic Fibrosis Research AdvancesFrench-language works237,207