Hydrostatic Pressure Stimulation of Human Mesenchymal Stem Cells Seeded on Collagen-Based Artificial Extracellular Matrices
Bibliographic record
Abstract
Human mesenchymal stem cells (hMSCs) from bone marrow are considered a promising cell source for bone tissue engineering applications because of their ability to differentiate into cells of the osteoblastic lineage. Mechanical stimulation is able to promote osteogenic differentiation of hMSC; however, the use of hydrostatic pressure (HP) has not been well studied. Artificial extracellular matrices containing collagen and chondroitin sulfate (CS) have promoted the expression of an osteoblastic phenotype by hMSCs. However, there has been little research into the combined effects of biochemical stimulation by matrices and simultaneous mechanical stimulation. In this study, artificial extracellular matrices generated from collagen and/or CS were coated onto polycaprolactone-co-lactide substrates, seeded with hMSCs and subjected to cyclic HP at various time points during 21 days after cell seeding to investigate the effects of biochemical, mechanical, and combined biochemical and mechanical stimulations. Cell differentiation was assessed by analyzing the expression of alkaline phosphatase (ALP) at the protein- and mRNA levels, as well as for calcium accumulation. The timing of HP stimulation affected hMSC proliferation and expression of ALP activity. HP stimulation after 6 days was most effective at promoting ALP activity. CS-containing matrices promoted the osteogenic differentiation of hMSCs. A combination of both CS-containing matrices and cyclic HP yields optimal effects on osteogenic differentiation of hMSCs on scaffolds compared with individual responses.
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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".