MiR-138-5p knockdown promotes osteogenic differentiation through FOXC1 up-regulation in human bone mesenchymal stem cells
Bibliographic record
Abstract
This study examined the hypothesis that the microRNA miR-138-5p reduces the osteodifferentiation of human bone mesenchymal stem cells (hBMSCs) by downregulating the expression of forkhead box C1 (FOXC1). For this, hBMSCs were separated from bone marrow and osteogenic induction medium was added to stimulate osteogenic differentiation. Flow cytometric analysis was applied to evaluate the expression of cell-surface antigens associated with hBMSCs, including CD29, CD44, CD90, CD45, and CD34. qRT-PCR assays and Western blot assays were used to measure the mRNA and protein expression of miR-138-5p, osteocalcin, runt-related transcription factor 2, bone sialoprotein, alkaline phosphatase (ALP), and FOXC1. ALP staining assays and Alizarin Red staining (ARS) assays were used to confirm osteogenic differentiation. We used a luciferase assay to test the interaction between miR-138-5p and FOXC1. We demonstrated that miR-138-5p is downregulated in osteogenic differentiated hBMSCs. Further, overexpression of miR-138-5p reduced the expression of markers for osteodifferentiation, ALP activity, and ARS activity. Furthermore, we showed that FOXC1 is a downstream target gene of miR-138-5p, and that knockdown of miR-138-5p improves the osteogenesis differentiation of hBMSCs by upregulating FOXC1. The results from this study indicate miR-138-5p as a new target for osteogenic differentiation of hBMSCs and the treatment of bone defects.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".