Both Human Hematoma Punctured from Pelvic Fractures and Serum Increase Muscle Resident Stem Cells Response to BMP9: A Multivariate Statistical Approach
Bibliographic record
Abstract
Hematoma and skeletal muscles play a crucial role in bone fracture healing. The muscle resident mesenchymal stromal cells (mrSCs) can promote bone formation by differentiating into osteoblasts upon treatment by bone morphogenetic proteins (BMP), such as BMP9. However, the influence of hematoma fracture extracts (Hema) on human mrSC (hmrSC) response to BMP9 is still unknown. We therefore determined the influence of Hema, human healthy serum (HH), and fetal bovine serum (FBS, control) on BMP9-induced osteoblast commitment of hmrSC by measuring alkaline phosphatase activity. Multiplex assays of 90 cytokines were performed to characterize HH and Hema composition and allow their classification by a multivariate statistical approach depending on their expression levels. We confirmed that BMP9 had a greater effect on osteoblastic differentiation of hmrSCs than BMP2 in presence of FBS. The hmrSCs response to BMP9 was enhanced by both Hema and HH, even though several cytokines were upregulated (IL-6, IL-8, MCP-1, VEGF-A and osteopontin), downregulated (BMP9, PDGF) or similar (TNF-alpha) in Hema compared with HH. Thus, hematoma may potentiate BMP9-induced osteogenic differentiation of hmrSCs during bone fracture healing. The multivariate statistical analyses will help to identify the cytokines involved in such phenomenon leading to normal or pathological bone healing.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".