Enhanced osteoblastic differentiation of mesenchymal stem cells seeded in RGD-functionalized PLLA scaffolds and cultured in a flow perfusion bioreactor
Bibliographic record
Abstract
The present study combines chemical and mechanical stimuli to modulate the osteogenic differentiation of mesenchymal stem cells (MSCs). Arg-Gly-Asp (RGD) peptides incorporated into biomaterials have been shown to upregulate MSC osteoblastic differentiation. However, these effects have been assessed under static culture conditions, while it has been reported that flow perfusion also has an enhancing effect on MSC osteoblastic differentiation. It is clear that there is a need to combine RGD modification of biomaterials with mechanical stimulation of MSCs via flow perfusion and evaluate its effects on MSC differentiation down the osteogenic lineage. In this study, the effect of different levels of RGD modification of poly(L-lactic acid) scaffolds on MSC osteogenesis was evaluated under conditions of flow perfusion. It was found that there is a synergistic enhancement of different osteogenic markers, due to the combination of flow perfusion and RGD surface modification when compared to their individual effects. Furthermore, under conditions of flow perfusion, there is an RGD surface concentration optimal for differentiation, and it is flow rate-dependent. This report underlines the significance of incorporating combined biomimesis via biochemical and mechanical microenvironments that modulate in vivo cell behaviour and tissue function for more efficient tissue-engineering strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".