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Record W2114277100 · doi:10.1177/0192623307312705

An Approach to Achieve Long-Term Expression in Skin Gene Therapy

2008· review· en· W2114277100 on OpenAlexfundno aff
Jean‐Philippe Therrien, Wolfgang Pfützner, Jonathan Vogel

Bibliographic record

VenueToxicologic Pathology · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Cancer InstituteNational Cancer Research Institute
KeywordsColchicineGenetic enhancementViral vectorCancer researchGene deliveryGeneMedicineSkin graftingGene expressionPharmacologyImmunologyBiologyInternal medicineSurgeryGenetics

Abstract

fetched live from OpenAlex

For gene therapy purposes, the skin is an attractive organ to target for systemic delivery of therapeutic proteins to treat systemic diseases, skin diseases, or skin cancer. To achieve long-term stable expression of a therapeutic gene in keratinocytes (KC), we have developed an approach using a bicistronic retroviral vector expressing the desired therapeutic gene linked to a selectable marker (multidrug resistant gene, MDR) that is then introduced into KC and fibroblasts (FB) to create genetically modified human skin equivalent (HSE). After grafting the HSE onto immunocompromised mice, topical colchicine treatment is used to select and enrich for genetically modified keratinocyte stem cells (KSC) that express MDR and are resistant to colchicine's antimitotic effects. Both the apparatus for topical colchicine delivery and the colchicine doses have been optimized for application to human skin. This approach can be validated by systemic delivery of therapeutic factors such as erythropoietin and the antihypertensive atrial natriuretic peptide.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.003

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 head0.064
GPT teacher head0.348
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2008
Admission routes1
Has abstractyes

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