Analysis of Exon Skipping Applicability for Dysferlinopathies
Bibliographic record
Abstract
gene and treat dysferlinopathies. Understanding the applicability of various exon skipping strategies in the total patient population, an analysis not previously performed, can help guide researchers in prioritizing therapies with the broadest potential impact. Using data from the UMD-DYSF database, we evaluated all reported pathogenic variants in dysferlinopathy patients for the applicability of single- or double-exon skipping approaches to exclude the pathogenic variants while maintaining the open reading frame. A total of 61 theoretically applicable exon skipping strategies were identified, with the potential to address 90.0% of the pathogenic variants reported-44.6% through single-exon skipping and 45.3% through double-exon skipping. The most broadly applicable targets include exons 28 and 29 (9.0%), exons 27 and 28 (6.7%), and exons 50 and 51 (5.4%). While numerous theoretically applicable strategies were identified, it remains unclear if the truncated proteins produced through each exon skipping strategy will have improved functionality to alleviate patient symptoms. Further preclinical studies and clinical trials will be essential to determine the effectiveness of these therapies, potentially expanding access to disease-modifying treatments for dysferlinopathy patients.
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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 | 0.000 |
| 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 | 0.000 | 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 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".