Directing hMSCs fate through geometrical cues and mimetics peptides
Bibliographic record
Abstract
The native microenvironment of mesenchymal stem cells (hMSCs)-the extracellular matrix (ECM), is a complex and heterogenous environment structured at different scales. The present study aims at mimicking the hierarchical microorganization of proteins or growth factors within the ECM using the photolithography technique. Polyethylene terephthalate substrates were used as a model material to geometrically defined regions of RGD + BMP-2 or RDG + OGP mimetic peptides. These ECM-derived ligands are under research for regulation of mesenchymal stem cells osteogenic differentiation in a synergic manner. The hMSCs osteogenic differentiation was significantly affected by the spatial distribution of dually grafted peptides on surfaces, and hMSCs cells reacted differently according to the shape and size of peptide micropatterns. Our study demonstrates the presence of a strong interplay between peptide geometric cues and stem cell differentiation toward the osteoblastic lineage. These tethered surfaces provide valuable tools to investigate stem cell fate mechanisms regulated by multiple ECM cues, thereby contributing to the design of new biomaterials and improving hMSCs differentiation cues.
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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".