A Synthetic Mechano Growth Factor E Peptide Enhances Myogenic Precursor Cell Transplantation Success
Bibliographic record
Abstract
Myogenic precursor cell (MPC) transplantation is a good strategy to introduce dystrophin expression in muscles of Duchenne muscular dystrophy (DMD) patients. Insulin-like growth factor (IGF-1) promotes MPC activities, such as survival, proliferation, migration and differentiation, which could enhance the success of their transplantation. Alternative splicing of the IGF-1 mRNA produces different muscle isoforms. The mechano growth factor (MGF) is an isoform, especially expressed after a mechanical stress. A 24 amino acids peptide corresponding to the C-terminal part of the MGF E domain (MGF-Ct24E peptide) was synthesized. This peptide had been shown to enhance the proliferation and delay the terminal differentiation of C(2)C(12) myoblasts. The present study showed that the MGF-Ct24E peptide improved human MPC transplantation by modulating their proliferation and differentiation. Indeed, intramuscular or systemic delivery of this synthetic peptide significantly promoted engraftment of human MPCs in mice. In vitro experiments demonstrated that the MGF-Ct24E peptide enhanced MPC proliferation by a different mechanism than the binding to the IGF-1 receptor. Moreover, MGF-Ct24E peptide delayed human MPC differentiation while having no outcome on survival. Those combined effects are probably responsible for the enhanced transplantation success. Thus, the MGF-Ct24E peptide is an interesting agent to increase MPC transplantation success in DMD patients.
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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 | 0.000 | 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".