Testing the efficacy of a human full-length OPG-Fc analog in a severe model of cardiotoxin-induced skeletal muscle injury and repair
Bibliographic record
Abstract
Although receptor-activator of nuclear factor κB (RANK), its ligand RANKL, and osteoprotegerin (OPG), which are members of the tumor necrosis factor (TNF) superfamily, were first discovered in bone cells, they are also expressed in other cells, including skeletal muscle. We previously showed that the RANK/RANKL/OPG pathway is involved in the physiopathology of Duchenne muscular dystrophy and that a mouse full-length OPG-Fc (mFL-OPG-Fc) treatment is superior to muscle-specific RANK deletion in protecting dystrophic muscles. Although mFL-OPG-Fc has a beneficial effect in the context of muscular dystrophy, the function of human FL-OPG-Fc (hFL-OPG-Fc) during muscle repair is not yet known. In the present study, we investigated the impacts of an hFL-OPG-Fc treatment following the intramuscular injection of cardiotoxin (CTX). We show that a 7-day hFL-OPG-Fc treatment improved force production of soleus muscle. hFL-OPG-Fc also improved soleus muscle integrity and regeneration by increasing satellite cell density and fiber cross-sectional area, attenuating neutrophil inflammatory cell infiltration at 3 and 7 days post-CTX injury, increasing the anti-inflammatory M2 macrophages 7 days post-CTX injury. hFL-OPG-Fc treatment also favored M2 over M1 macrophage phenotypic polarization in vitro . We show for the first time that hFL-OPG-Fc improved myotube maturation and fusion in vitro and reduced cytotoxicity and cell apoptosis. These findings demonstrate that hFL-OPG-Fc has therapeutic potential for muscle diseases in which repair and regeneration are impaired.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".