GROWTH FACTORS IMPROVE THE IN VIVO MIGRATION OF HUMAN SKELETAL MYOBLASTS BY MODULATING THEIR ENDOGENOUS PROTEOLYTIC ACTIVITY
Bibliographic record
Abstract
BACKGROUND: A main technological problem related to the clinical application of myoblast transplantation is the poor migration of transplanted cells. In this study, we investigated a new physiologic approach that consists of coinjecting motogenic factors insulin growth factor (IGF)-1 or basic fibroblast growth factor (bFGF) to enhance the migration of human skeletal myoblasts. Among the different ways by which those factors can induce the cell migration processes, we investigated their capacity to enhance cell endogenous proteolytic activity that will help transplanted cells to migrate through the extracellular matrix. METHODS: In vitro, myoblasts were coincubated with bFGF or IGF-1. Growth factors effects on cell migration were evaluated using invasion chambers, and their effects on proteolytic systems were evaluated by zymography, Western blot, and reverse transcription polymerase chain reaction. In vivo, myoblasts were coinjected with growth factors and the intramuscular migration capacity was assessed using the microtube technique. RESULTS: In vitro, the presence of IGF-1 or bFGF significantly enhanced the expression of the gelatinase matrix metalloproteinase-9 and focalized the fibrinolytic system activity at the cell membrane. In vitro and in vivo, both bFGF and IGF-1 showed strong chemokinetic potentials and improved the migration of human myoblasts. Moreover, the implication some proteinases in the in vivo enhanced migration was confirmed using specific inhibitors (BB94 or amiloride). CONCLUSIONS: These results suggest that IGF-1 or bFGF coinjection with human myoblasts increased their proteolytic activities and consequently their migratory capacity. This study may help to develop approaches that will reduce the number of injection sites for the treatment of Duchenne muscular dystrophy patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".