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
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".