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Record W1973047855 · doi:10.1089/hum.2010.024

Electrotransfer of the Full-Length Dog Dystrophin into Mouse and Dystrophic Dog Muscles

2010· article· en· W1973047855 on OpenAlexaff
Christophe Pichavant, Pierre Chapdelaine, Daniel Giuliano Cerri, João C. S. Bizário, Jacques P. Tremblay

Bibliographic record

VenueHuman Gene Therapy · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlanarian Biology and Electrostimulation
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsDystrophinDuchenne muscular dystrophyMuscular dystrophyBiologymdx mousePlasmidUtrophinMolecular biologyComplementary DNAMuscle disorderPhenotypeMyocyteCell biologyGeneGeneticsInternal medicineMedicine

Abstract

fetched live from OpenAlex

Duchenne muscular dystrophy (DMD) is an X-linked genetic disease characterized by the absence of dystrophin (427 kDa). An approach to eventually restore this protein in patients with DMD is to introduce into their muscles a plasmid encoding dystrophin cDNA. Because the phenotype of the dystrophic dog is closer to the human phenotype than is the mdx mouse phenotype, we have studied the electrotransfer of a plasmid carrying the full-length dog dystrophin (FLDYS(dog)) in dystrophic dog muscle. To achieve this nonviral delivery, the FLDYS(dog) cDNA was cloned in two plasmids containing either a cytomegalovirus or a muscle creatine kinase promoter. In both cases, our results showed that the electrotransfer of these large plasmids (∼17 kb) into mouse muscle allowed FLDYS(dog) expression in the treated muscle. The electrotransfer of pCMV.FLDYS(dog) in a dystrophic dog muscle also led to the expression of dystrophin. In conclusion, introduction of the full-length dog dystrophin cDNA by electrotransfer into dystrophic dog muscle is a potential approach to restore dystrophin in patients with DMD. However, the electrotransfer procedure should be improved before applying it to humans.

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 score0.024
Threshold uncertainty score0.420

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 science0.0000.000
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 head0.007
GPT teacher head0.239
Teacher spread0.233 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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