Macrophages Inhibit Migration, Metabolic Activity and Osteogenic Differentiation of Human Mesenchymal Stem Cells in vitro
Bibliographic record
Abstract
To better elucidate the role of macrophages in bone morphogenetic protein (BMP)-induced bone repair, this study evaluated the effects of macrophages on the migration, metabolic activity and BMP-2-induced osteogenic differentiation of human mesenchymal stem cells (hMSCs). Human monocytes were induced into a macrophage phenotype, and the conditioned media (CM) from undifferentiated monocytes and differentiated macrophages were collected for treatment of hMSCs. Expression levels of osteoblastic marker genes, alkaline phosphatase (ALP) activity and mineral deposition were assessed. The migration of hMSCs was significantly decreased after treatment with the macrophage CM (but not monocyte CM), in a dose-dependent manner. Significant inhibition of hMSC metabolism was observed on days 3 and 7 after treatment with the macrophage CM. The osteoblastic marker genes analyzed (ALP, bone sialoprotein, osteocalcin and runt-related transcription factor-2) after exposure of hMSCs to BMP-2 were all significantly downregulated in cells treated with the macrophage CM. The hMSCs treated with macrophage CM showed significantly decreased enzymatic activity of ALP and calcium content compared with those treated with monocyte CM or basal medium. High levels of interleukin-1β and tumor necrosis factor-α found in macrophage CM may mediate these observed effects on hMSCs. We conclude that macrophage CM suppressed the BMP-2-induced osteogenic differentiation of hMSCs, suggesting that macrophages might contribute to decreased osteogenic effects of BMPs in a clinical setting.
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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".