Three‐dimensional‐printed polycaprolactone/polypyrrole conducting scaffolds for differentiation of human olfactory <scp>ecto‐mesenchymal</scp> stem cells into Schwann cell‐like phenotypes and promotion of neurite outgrowth
Bibliographic record
Abstract
Implantation of a suitable nerve guide conduit (NGC) seeded with sufficient Schwann cells (SCs) is required to improve peripheral nerve regeneration efficiently. Given the limitations of isolating and culturing SCs, using various sources of stem cells, including mesenchymal stem cells (MSCs) obtained from nasal olfactory mucosa, can be desirable. Olfactory ecto-MSCs (OE-MSCs) are a new population of neural crest-derived stem cells that can proliferate and differentiate into SCs and can be considered a promising autologous alternative to produce SCs. Regardless, a biomimetic physicochemical microenvironment in NGC such as electroconductive substrate can affect the fate of transplanted stem cells, including differentiation toward SCs and nerve regeneration. Therefore, in this study, the effect of 3D printed polycaprolactone (PCL)/polypyrrole (PPy) conductive scaffolds on differentiation of human OE-MSCS into functional SC-like phenotypes was investigated. Biological evaluation of 3D printed scaffolds was examined by in vitro culturing the OE-MSCs on samples surfaces, and conductivity showed no effect on increased cell attachment, proliferation rate, viability, and distribution. In contrast, immunocytochemical staining and real-time polymerase chain reaction analysis indicated that 3D structures coated with PPy could provide a favorable microenvironment for OE-MSCs differentiation. In addition, it was found that differentiated OE-MSCs within PCL/PPy could secrete the highest amounts of nerve growth factor and brain-derived neurotrophic factor neurotrophic factors compared to pure PCL and 2D culture. After co-culturing with PC12 cells, a significant increase in neurite outgrowth on PCL/PPy conductive scaffold seeded with differentiated OE-MSCs. These findings indicated that 3D printed PCL/PPy conductive scaffold could support differentiation of OE-MSCs into SC-like phenotypes to promote neurite outgrowth, suggesting their potential for neural tissue engineering applications.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".