Different Effects of Insulin-Like Growth Factor-1 and Insulin-Like Growth Factor-2 on Myogenic Differentiation of Human Mesenchymal Stem Cells
Bibliographic record
Abstract
Insulin-like growth factors (IGFs) are critical components of the stem cell niche, as they regulate proliferation and differentiation of stem cells into different lineages, including skeletal muscle. We have previously reported that insulin-like growth factor binding protein-6 (IGFBP-6), which has high affinity for IGF-2, alters the differentiation process of placental mesenchymal stem cells (PMSCs) into skeletal muscle. In this study, we determined the roles of IGF-1 and IGF-2 and their interactions with IGFBP-6. We showed that IGF-1 increased IGFBP-6 levels within 24 hours but decreased after 3 days, while IGF-2 maintained higher levels of IGFBP-6 throughout myogenesis. IGF-1 increased IGFBP-6 in the early phase as a requirement for muscle commitment. In contrast, IGF-2 enhanced muscle differentiation as shown by the expression of muscle differentiation markers MyoD, MyoG, and MHC. IGF-1 and IGF-2 had different effects on muscle differentiation with IGF-1 promoting early commitment to muscle and IGF-2 promoting complete muscle differentiation. We also showed that PMSCs acquired increasing capacity to synthesize IGF-2 during muscle differentiation, and the capacity increased as the differentiation progressed suggesting an autocrine and/or paracrine effect. Additionally, we demonstrated that IGFBP-6 could enhance the muscle differentiation process in the absence of IGF-2.
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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".