Engineered substrates with imprinted cell-like topographies induce direct differentiation of adipose-derived mesenchymal stem cells into Schwann cells
Bibliographic record
Abstract
Differentiation of stem cells to Schwann is considered efficient way for nerve regeneration since the sources of human Schwann cells are limited for clinical application. It is demonstrated that mimicking micromechanical forces or micro/nanotopographical environments that stem cells are experienced in vivo could control their fate. Here, the potency of substrates with imprinted cell-like topographies for direct differentiation of adipose-derived mesenchymal stem cells (ADSCs) into Schwann cells (SCs) is reported. For the preparation of substrates with imprinted SC-Like topographies, SCs are isolated from the sciatic nerve, grown, fixed, and then SC morphologies are transferred to polydimethylsiloxane (PDMS) substrates by mold casting. Subsequently, mesenchymal stem cells (MSCs) are seeded on the SC-imprinted substrates and their differentiation to SCs is evaluated by immunocytochemistry, real-time PCR, and western blotting. Analysis of morphology and expression of SC-specific markers show that MSCs cultured on the imprinted substrates have the typical SC-like morphology and express SC-specific markers including S100b, p75NTR, and Sox10. It is believed that specific cell-like topographies and related micromechanical cues can be sufficient for direct differentiation of ADSCs into Schwann cells by cell-imprinting method as a physical technique.
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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".