Sustained Improvement of Muscle Function One Year After Full-Length Dystrophin Gene Transfer into <i>mdx</i> Mice by a Gutted Helper-Dependent Adenoviral Vector
Bibliographic record
Abstract
Dystrophin gene transfer using helper-dependent adenoviral vectors (HDAd) deleted of all viral genes is a promising option to treat muscles in Duchenne muscular dystrophy (DMD). Previously, we reported high-level dystrophin expression and functional correction of dystrophin-deficient (mdx) mouse muscle 60 days after gene transfer with an HDAd encoding two full-length murine dystrophin cDNAs (referred to as HDCBDysM). In the present study, we tested the long-term efficacy of HDCBDysM by examining muscle contractility parameters and the stability of dystrophin expression 1 year after injection into neonatal mdx muscles. At this point, HDCBDysM-treated muscles averaged 52% dystrophin-positive fibers. Treated muscles also displayed significantly greater isometric force production as well as greater resistance to the force deficits and damage caused by eccentric contractions. The level of protection against eccentric contraction-induced force deficits correlated with the percentage of dystrophin-positive fibers. Furthermore, HDCBDysM treatment restored the dystrophin-glycoprotein complex (DGC) to the sarcolemma and improved other aspects of mdx muscle histopathology examined (central nucleation, muscle hypertrophy, and mononuclear [phagocytic] cell infiltration). These improvements occurred despite the induction of a humoral response against murine dystrophin. Our results indicate that major therapeutic benefits of HDCBDysM are maintained for a long period of the animals' lifespan and suggest that HDCBDys holds promise for treating DMD by gene therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".