Controlling differentiation of stem cells <i>via</i> bioactive disordered cues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it