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Record W2279569345 · doi:10.1089/hum.2015.108

Long-Term Expression of the Human <i>CFTR</i> Gene in Mouse Airway via Helper-Dependent Adenoviral Vector Delivery and Transient Immunosuppression

2015· article· en· W2279569345 on OpenAlexafffund
Huibi Cao, Jing Wu, Cathleen Duan, Kai Du, Chan Mi Lee, Herman Yeger, Jim Hu

Bibliographic record

VenueHuman Gene Therapy · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchCystic Fibrosis Canada
KeywordsGenetic enhancementImmunosuppressionTransgeneViral vectorImmune systemBiologyGene deliveryImmunologyAzathioprineReporter geneCTL*Gene expressionCancer researchMedicineGeneInternal medicineCD8Recombinant DNA

Abstract

fetched live from OpenAlex

Sustained expression of the CFTR gene is a major challenge to gene therapy with either viral or nonviral vectors with immune response to vector and transgene products. One strategy to achieve sustained CFTR expression is to modulate the host immune system through transient immunosuppression. In this study, we examined cyclophosphamide (cytoxan), dexamethasone (Dex), and a combination of cyclosporin, methylprednisolone, and azathioprine (combination) for their effects on long-term expression of the human CFTR delivered with helper-dependent adenoviral vectors in mouse airways. We found that cyclophosphamide significantly enhanced long-term expression of the transgenic human CFTR and the reporter gene LacZ by reducing host immune responses. Dex administration greatly reduced neutralizing antibody production but had no effect on transgene expression. Treatment with a combination of cyclosporin A, azathioprine, and methylprednisolone affected neither CFTR gene expression nor inflammation. Our data suggest that transient immunosuppression might be a strategy to improve sustained expression in gene therapy.

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 score0.011
Threshold uncertainty score0.907

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 science0.0010.000
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 head0.029
GPT teacher head0.292
Teacher spread0.263 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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