MétaCan
Menu
Back to cohort
Record W1990425673 · doi:10.1002/jgm.1501

Development of an inflammation‐inducible gene expression system using helper‐dependent adenoviral vectors

2010· article· en· W1990425673 on OpenAlexafffund
Tianyao Yang, Rongqi Duan, Huibi Cao, Benjamin H. Lee, Chun Xia, Zhijie Chang, A. Keith Tanswell, Jim Hu

Bibliographic record

VenueThe Journal of Gene Medicine · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health ResearchFoundation Fighting BlindnessCystic Fibrosis Foundation
KeywordsGenetic enhancementTransgeneGeneGene expressionInflammationReporter geneBiologyGene deliveryVectors in gene therapyViral vectorIn vivoVector (molecular biology)Cancer researchImmunologyRecombinant DNAGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical studies have shown that gene therapy is a promising approach for treating such genetic diseases as the eye disease, Leber's congenital amaurosis. Development of gene therapy approaches for treating chronic inflammatory diseases is, however, more challenging because it requires the production of anti-inflammatory molecules at the diseased tissues only when they are needed. METHODS: We designed such a system by modifying the human interleukin (IL)-6 gene promoter to direct transgene expression and delivered the system into cultured cells as well as mouse lungs using a helper-dependent adenoviral vector. RESULTS: We have demonstrated both in vitro and in vivo that the reporter LacZ or human IL-10 gene can be induced by inflammatory stimuli. CONCLUSIONS: The results obtained indicate that the inflammation inducible gene expression system based on the modified human IL-6 gene promoter has the potential to be used for developing gene therapy for treating inflammatory diseases.

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)0.003
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 score0.004
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.030
GPT teacher head0.315
Teacher spread0.285 · 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

Citations8
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Gene MedicineSame topicVirus-based gene therapy researchFrench-language works237,207