A Novel Melt Electrospinning System for Studying Cell Substrate Interactions
Bibliographic record
Abstract
Controlling cell behavior has generated immense attention in the fields of tissue engineering and regenerative medicine. Particular emphasis has been given to the creation of 3D biomimetic cellular microenvironments that replicate the complex nature of the extracellular matrix (ECM). A key factor that has not been rigorously deconstructed using scalable, layered manufacturing approaches is the structural dimension or scale aspect of in vitro culture models. Melt electrospinning represents a bio-additive manufacturing process that has been relatively under-reported. Although complex in nature, the melt electrospinning process can furnish a 3D cell delivery format with physiologically relevant 3D structural cues. In the present work, poly-ε-caprolactone (PCL) has been chosen as the biomaterial substrate. Rheological studies that guide the design phase of the reported system have been performed for the entire PCL melt processing range, implicating the governing effect of the experimental melt temperature on the scale and the topography in the final processed material. Notable challenges that arise from the nature of the process with respect to the electrospun fiber stability and resolution have been overcome through the design of a novel heating element configuration. In this paper, a reliable biofabrication process with tunable processing of the fiber diameter and alignment is reported. Fundamental parametric studies utilizing the major processing parameters demonstrate the potential for the system to precisely fabricate 3D PCL scaffolds with microstructural features.
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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".