Low-Frequency Mechanical Stimulation Modulates Osteogenic Differentiation of C2C12 Cells
Bibliographic record
Abstract
Mechanical stimulation influences stem cell differentiation and may therefore provide improved lineage specification control for clinical applications. Low-frequency oscillatory mechanical stimulation (0.01 Hz) has recently been shown to suppress adipogenic differentiation of mesenchymal stem cells, indicating that the range of effective stimulation frequencies is not limited to those associated with locomotion, circulation, and respiration. We hypothesized that low-frequency mechanical stimulation (0.01 Hz) can also promote osteogenic cell differentiation of myoblastic C2C12 cells in combination with BMP-2. Results indicate that low-frequency mechanical stimulation can significantly enhance osteogenic gene expression, provided that differentiation is initiated by a priming period involving BMP-2 alone. Subsequent application of low-frequency mechanical stimulation appears to act synergistically with continued BMP-2 exposure to promote osteogenic differentiation of C2C12 cells and can even partially compensate for the removal of BMP-2. These effects may be mediated by the ERK and Wnt signalling pathways. Osteogenic induction of C2C12 cells by low-frequency mechanical stimulation is therefore critically dependent upon previous exposure to growth factors, and the timing of superimposed BMP-2 and mechanical stimuli can sensitively influence osteogenesis. These insights may provide a technically simple means for control of stem cell differentiation in cell-based therapies, particularly for the enhancement of differentiation toward desired lineages.
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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".