Immortalized Muscle Cell Model to Test the Exon Skipping Efficacy for Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a lethal genetic disorder that most commonly results from mutations disrupting the reading frame of the dystrophin (DMD) gene. Among the therapeutic approaches employed, exon skipping using antisense oligonucleotides (AOs) is one of the most promising strategies. This strategy aims to restore the reading frame, thus producing a truncated, yet functioning dystrophin protein. In 2016, the Food and Drug Administration (FDA) conditionally approved the first AO-based drug, eteplirsen (Exondys 51), developed for DMD exon 51 skipping. An accurate and reproducible method to quantify exon skipping efficacy is essential for evaluating the therapeutic potential of different AOs sequences. However, previous in vitro screening studies have been hampered by the limited proliferative capacity and insufficient amounts of dystrophin expressed by primary muscle cell lines that have been the main system used to evaluate AOs sequences. In this paper, we illustrate the challenges associated with primary muscle cell lines and describe a novel approach that utilizes immortalized cell lines to quantitatively evaluate the exon skipping efficacy in in vitro studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".