Effect of growth factors and extracellular matrix materials on the proliferation and differentiation of microencapsulated myoblasts
Bibliographic record
Abstract
An alternative approach to gene therapy via non-autologous somatic gene therapy is to implant genetically-engineered cells protected from immune rejection with microcapsules to deliver a therapeutic gene product. This delivery system may be optimized by using myoblast cell lines which can undergo terminal differentiation into myotubes, thus removing the potential problems of tumorigenesis and space restriction. However, once encapsulated, myoblasts do not proliferate or differentiate well. We now report the use of extracellular matrix components and growth factors to improve these properties. Addition of matrix material collagen, merosin or laminin all stimulated myoblast proliferation, particularly when merosin and laminin were combined; however, none, except collagen, stimulated differentiation. Inclusion of basic fibroblast growth factor (bFGF) within the microcapsules in the presence of collagen stimulated proliferation of C2C12 myoblasts, as well as differentiation into myotubes. Inclusion of insulin growth factor (IGF-II) in the microcapsules had no effect on proliferation but accelerated myoblasts differentiation. When the above matrix material and growth factors were provided in combination, the use of merosin and laminin together with bFGF and IGF-II stimulated myoblast proliferation but had no effect on differentiation. In contrast, the cocktail containing bFGF, IGF-II and collagen induced increased myoblasts proliferation and subsequent differentiation. Hence, the combination of bFGF, IGF-II and collagen appears optimal in improving proliferation and differentiation in encapsulated myoblasts.
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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".