Therapeutic Applications of CRISPR/Cas for Duchenne Muscular Dystrophy
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Duchenne muscular dystrophy (DMD) is an X-linked neuromuscular disease caused by the lack of dystrophin due to mutations in the DMD gene. Since dystrophin is essential in maintaining the integrity of the sarcolemmal membrane, the absence of the protein leads to muscle damage and DMD disease manifestation. Currently, there is no cure with only symptomatic management available. OBJECTIVE: The most recent advancements in DMD therapies do not provide a permanent treatment for DMD. CRISPR/Cas technology poses as an attractive platform for DMD gene therapy both dependent and independent of the specific mutation. METHOD: CRISPR/Cas technology can be utilized independent of the patient mutation by modulating disease modifiers. Regarding DMD duplication mutations, full length dystrophin can be restored using a single sgRNA approach. For DMD deletion and point mutations, the open reading frame (ORF) can be restored by removing or reframing exon(s) to produce a shorter form of dystrophin. The full-length wildtype dystrophin can also be restored using homologous recombination (HR). The CRISPR/Cas components for these strategies were delivered in vivo using the adeno-associated virus (AAV) vector. RESULTS: The upregulation of a dystrophin homologue called utrophin can compensate for the lack of dystrophin protein, and has been successfully demonstrated in patient cells. Full-length dystrophin was restored in patient cells carrying duplication mutations. The shorter form and full-length dystrophin was recovered using CRISPR strategies in vitro and in vivo. CONCLUSIONS: Restoration of the wild type and shorter form of dystrophin highlights the therapeutic potential of CRISPR technology for DMD.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it