Targeting fibrosis in the Duchenne Muscular Dystrophy mice model: an uphill battle
Bibliographic record
Abstract
Abstract Aim Fibrosis is the most common complication from chronic diseases, and yet no therapy capable of mitigating its effects is available. Our goal is to unveil specific signallings regulating the fibrogenic process and to identify potential small molecule candidates that block fibrogenic differentiation of fibro/adipogenic progenitors. Method We performed a large-scale drug screen using muscle-resident fibro/adipogenic progenitors from a mouse model expressing EGFP under the Collagen1a1 promotor. We first confirmed that the EGFP was expressed in response to TGFβ1 stimulation in vitro . Then we treated cells with TGFβ1 alone or with drugs from two libraries of known compounds. The drugs ability to block the fibrogenic differentiation was quantified by imaging and flow cytometry. From a two-rounds screening, positive hits were tested in vivo in the mice model for the Duchenne muscular dystrophy (mdx mice). The histopathology of the muscles was assessed with picrosirius red (fibrosis) and laminin staining (myofiber size). Key findings From the in vitro drug screening, we identified 21 drugs and tested 3 in vivo on the mdx mice. None of the three drugs significantly improved muscle histopathology. Significance The in vitro drug screen identified various efficient compounds, none of them strongly inhibited fibrosis in skeletal muscle of mdx mice. To explain these observations, we hypothesize that in Duchenne Muscular Dystrophy, in which fibrosis is a secondary event due to chronic degeneration and inflammation, the drugs tested could have adverse effect on regeneration or inflammation, balancing off any positive effects and leading to the absence of significant results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".