High mobility group box 1 (HMGB1) is a potential disease biomarker in cell and mouse models of Duchenne muscular dystrophy
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a progressive muscle wasting disorder affecting 1:3500 male births and is associated with myofiber degeneration, regeneration, and inflammation. Glucocorticoid treatments have been the standard of care due to immunomodulatory/immunosuppressive properties but novel genetic approaches, including exon skipping and gene replacement therapy, are currently being developed. The identification of additional biomarkers to assess DMD-related inflammatory responses and the potential efficacy of these therapeutic approaches are thus of critical importance. The current study uses RNA sequencing of skeletal muscle from two mdx mouse models to identify high mobility group box 1 (HMGB1) as a candidate biomarker potentially contributing to DMD-related inflammation. HMGB1 protein content was increased in a human iPSC-derived skeletal myocyte model of DMD and microdystrophin treatment decreased HMGB1 back to control levels. In vivo, HMGB1 protein levels were increased in vehicle treated B10-mdx skeletal muscle compared to B10-WT and significantly decreased in B10-mdx animals treated with adeno-associated virus (AAV)-microdystrophin. However, HMGB1 protein levels were not increased in D2-mdx skeletal muscle compared to D2-WT, demonstrating a strain-specific difference in DMD-related immunopathology.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".