Individual transcriptomic response to strength training for patients with myotonic dystrophy type 1
Bibliographic record
Abstract
Myotonic dystrophy type 1 (DM1), the most common form of adult-onset muscular dystrophy, is caused by a CTG expansion resulting in significant transcriptomic dysregulation that leads to muscle weakness and wasting. While strength training is clinically beneficial in DM1, molecular effects had not been studied. To determine whether training rescued transcriptomic defects, RNA-Seq was performed on vastus lateralis samples from 9 male patients with DM1 before and after a 12-week strength-training program and 6 male controls who did not undergo training. Differential gene expression and alternative splicing analysis were correlated with the one-repetition maximum strength evaluation method (leg extension, leg press, hip abduction, and squat). While training program-induced improvements in splicing were similar among most individuals, rescued splicing events varied considerably between individuals. Gene expression improvements were highly varied between individuals, and the percentage of differentially expressed genes rescued after training were strongly correlated with strength improvements. Evaluating transcriptome changes individually revealed responses to the training not evident from grouped analysis, likely due to disease heterogeneity and individual exercise response differences. Our analyses indicate that transcriptomic changes are associated with clinical outcomes in patients with DM1 undergoing training and that these changes are often specific to the individual and should be analyzed accordingly.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".