A Fibrin Matrix Promotes the Differentiation of EMSCs Isolated from Nasal Respiratory Mucosa to Myelinating Phenotypical Schwann-Like Cells
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Bibliographic record
Abstract
Because Schwann cells perform the triple tasks of myelination, axon guidance and neurotrophin synthesis, they are candidates for cell transplantation that might cure some types of nervous-system degenerative diseases or injuries. However, Schwann cells are difficult to obtain. As another option, ectomesenchymal stem cells (EMSCs) can be easily harvested from the nasal respiratory mucosa. Whether fibrin, an important transplantation vehicle, can improve the differentiation of EMSCs into Schwann-like cells (SLCs) deserves further research. EMSCs were isolated from rat nasal respiratory mucosa and were purified using anti-CD133 magnetic cell sorting. The purified cells strongly expressed HNK-1, nestin, p75(NTR), S-100, and vimentin. Using nuclear staining, the MTT assay and Western blotting analysis of the expression of cell-cycle markers, the proliferation rate of EMSCs on a fibrin matrix was found to be significantly higher than that of cells grown on a plastic surface but insignificantly lower than that of cells grown on fibronectin. Additionally, the EMSCs grown on the fibrin matrix expressed myelination-related molecules, including myelin basic protein (MBP), 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNPase) and galactocerebrosides (GalCer), more strongly than did those grown on fibronectin or a plastic surface. Furthermore, the EMSCs grown on the fibrin matrix synthesized more neurotrophins compared with those grown on fibronectin or a plastic surface. The expression level of integrin in EMSCs grown on fibrin was similar to that of cells grown on fibronectin but was higher than that of cells grown on a plastic surface. These results demonstrated that fibrin not only promoted EMSC proliferation but also the differentiation of EMSCs into the SLCs. Our findings suggested that fibrin has great promise as a cell transplantation vehicle for the treatment of some types of nervous system diseases or injuries.
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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 | 0.000 | 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) | 0.000 | 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