Myofibroblastic Differentiation of Human Skeletal Muscle‐Derived Progenitors Is Inhibited by Pirfenidone
Bibliographic record
Abstract
We identified a population within the human skeletal muscle (CD90+) capable of differentiating into myofibroblasts, the most important cells involved in fibrosis development, after stimulation with transforming growth factorbeta (TGFβ). Our objective was to characterize these progenitors and to evaluate the potential of Pirfenidone, a drug currently used for the treatment of idiopathic pulmonary fibrosis, to inhibit their myofibroblastic differentiation. Myofibroblastic differentiation was assessed with or without Pirfenidone treatment by measuring the expression of α‐smooth muscle actin (αSMA) and collagen type I at gene and protein levels as well as contractility. Pirfenidone inhibited the myofibroblastic differentiation as shown by a decrease of αSMA and collagen expression, lowered contractility and an inhibition of pro‐fibrotic genes. Suppression of Smad‐2 and ‐3 phosphorylation was shown to be involved as a mechanism through which Pirfenidone hindered the formation of myofibroblasts. Identifying myofibroblast progenitors in human skeletal muscle and showing the anti‐fibrotic effects of Pirfenidone is a step forward to better understand and treat muscle fibrosis observed in several muscle regenerative disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".