Changes in Physiopathological Markers in Myotonic Dystrophy Type 1 Skeletal Muscle: A 3-Year Follow-up Study
Bibliographic record
Abstract
Background: Myotonic dystrophy type 1 (DM1) is a slowly progressive disease caused by abnormal CTG repetitions on the dystrophia myotonica protein kinase (DMPK) gene. Long mRNA from CTG repetitions stabilizes in nuclear foci and sequester muscleblind-like splicing regulator 1 (MBNL1). Cardinal signs of DM1 include muscle wasting and weakness. The impacts of DM1 progression on skeletal muscle are under-researched. Objective: Identifying physiopathological markers related to maximal strength loss over time in DM1. Methods: Twenty-two individuals with DM1 participated in two maximal isometric muscle strength (MIMS) evaluations of their knee extensors and two vastus lateralis muscle biopsies, 3 years apart. Muscle fiber typing, size (including minimal Feret's diameter [MFD] and atrophy/hypertrophy factors [AF/HF]), and nuclear foci and MBNL1 colocalization (foci/MBNL1+) were evaluated. Immunoblotting was used to measure glycogen synthase kinase-3 beta (GSK3β), p62, LC3BI, LC3BII, and oxidative phosphorylation proteins. Results: There are significant correlations between the fold changes of MIMS with type 1 fiber MFD (ρ= 0.483) and AF (ρ= -0.514). Regression analysis shows that baseline percentage of foci/MBNL1+ nuclei and strength training explain 44.1% of foci/MBNL1+ nuclei percentage variation over time. There are fair to excellent correlations between the fold changes of MIMS and GSK3β (ρ= 0.327), p62 (ρ= 0.473), LC3BI (ρ= 0.518), LC3BII (ρ= -0.391) and LC3BII/LC3BI (ρ= -0.773). Conclusion: Type 1 MFD decrease and AF increase are correlated with MIMS loss. There seems to be a plateau effect in foci/MBNL1+ nuclei accumulation and strength training helps decrease this accumulation. Autophagy marker LC3BII/LC3BI ratio has a good biomarker potential of MIMS loss, but more investigations are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".