Fabrication of poly (ϵ-caprolactone) microfiber scaffolds with varying topography and mechanical properties for stem cell-based tissue engineering applications
Bibliographic record
Abstract
Highly porous poly (ϵ-caprolactone) microfiber scaffolds can be fabricated using electrospinning for tissue engineering applications. Melt electrospinning produces such scaffolds by direct deposition of a polymer melt instead of dissolving the polymer in a solvent as performed during solution electrospinning. The objective of this study was to investigate the significant parameters associated with the melt electrospinning process that influence fiber diameter and scaffold morphology, including processing temperature, collection distance, applied, voltage and nozzle size. The mechanical properties of these microfiber scaffolds varied with microfiber diameter. Additionally, the porosity of scaffolds was determined by combining experimental data with mathematical modeling. To test the cytocompatability of these fibrous scaffolds, we seeded neural progenitors derived from murine R1 embryonic stem cell lines onto these scaffolds, where they could survive, migrate, and differentiate into neurons; demonstrating the potential of these melt electrospun scaffolds for tissue engineering applications.
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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) | 0.000 | 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 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".