Editorial: Genome and transcriptome editing to understand and treat neuromuscular diseases
Bibliographic record
Abstract
Neuromuscular diseases such as Duchenne muscular dystrophy and facioscapulohumeral muscular 14 dystrophy are debilitating conditions that affect millions of individuals worldwide. In recent years, 15 there has been a growing interest in the use of genome and transcriptome editing techniques to 16 understand and treat these diseases. This research topic brings together four articles that highlight the 17 latest advances in this field. 18 19The these diseases can be considered for the first time. They also highlight that the lack of effective 67 therapy for muscular dystrophies can be explained by the fact that more than 40 genes have been 68described to be involved in these diseases, resulting in a wide range of abnormalities and with the 69 large size of the mutated genes, it challenges classical gene replacement therapies. 70Overall, these articles demonstrate the potential of genome and transcriptome editing techniques to 71 improve the understanding and treatment of neuromuscular diseases. With the rapid pace of 72 technological advancements in this field, it is likely that we will see even more exciting 73 developments in the near future. 74
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".