Controlling differentiation of stem cells <i>via</i> bioactive disordered cues
Bibliographic record
Abstract
, the cellular microenvironment has a crucial impact on regulating cell behavior and functions. A PET surface was activated and then functionalized with mimetic peptides to promote human mesenchymal stem cell (hMSC) adhesion and differentiation into an osteogenic lineage. Spray technology was used to randomly micropattern peptides (RGD and BMP-2 mimetic peptides) on the PET surface. The distribution of the peptides grafted on the surface, the roughness of the surfaces and the chemistry of the surfaces in each step of the treatment were ascertained by atomic force microscopy, fluorescence microscopy, time-of-flight secondary ion mass spectrometry, Toluidine Blue O assay, and X-ray photoelectron spectroscopy. Subsequently, cell lineage differentiation was evaluated by quantifying the expression of immunofluorescence markers: osteoblast markers (Runx-2, OPN) and osteocyte markers (E11, DMP1, and SOST). In this article, we hypothesized that a unique combination of bioactive micro/nanopatterns on a polymer surface improves the rate of morphology change and enhances hMSC differentiation. In DMEM, after 14 days, disordered micropatterned surfaces with RGD and BMP-2 led to a higher osteoblast marker expression than surfaces with a homogeneous dual peptide conjugation. Finally, hMSCs cultured in osteogenic differentiation medium (ODM) showed accelerated cell differentiation. In ODM, our results highlighted the expression of osteocyte markers when hMSCs were seeded on PET surfaces with random micropatterns.
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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".