Nonpulsed Sinusoidal Electromagnetic Fields as a Noninvasive Strategy in Bone Repair: The Effect on Human Mesenchymal Stem Cell Osteogenic Differentiation
Bibliographic record
Abstract
In vivo control of osteoblast differentiation is an important process needed to maintain the continuous supply of mature osteoblast cells for growth, repair, and remodeling of bones. The regulation of this process has also an important and significant impact on the clinical strategies and future applications of cell therapy. In this article, we studied the effect of nonpulsed sinusoidal electromagnetic field radiation tuned at calcium-ion cyclotron frequency of 50 Hz exposure treatment for bone differentiation of human mesenchymal stem cells (hMSCs) alone or in synergy with dexamethasone, their canonical chemical differentiation agent. Five days of continuous exposure to calcium-ion cyclotron resonance affect hMSC proliferation, morphology, and cytoskeletal actin reorganization. By quantitative real-time polymerase chain reaction, we also observed an increase of osteoblast differentiation marker expression such as Runx2, alkaline phosphatase (ALP), osteocalcin (OC), and osteopontin (OPN) together with the osteoprotegerin mRNA modulation. Moreover, in these cells, the increase of the protein expression of OPN and ALP was also demonstrated. These results demonstrate bone commitment of hMSCs through a noninvasive and biocompatible differentiating physical agent treatment and highlight possible applications in new regenerative medicine protocols.
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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".