3D Non-Woven Polyvinylidene Fluoride Scaffolds: Fibre Cross Section and Texturizing Patterns Have Impact on Growth of Mesenchymal Stromal Cells
Bibliographic record
Abstract
Several applications in tissue engineering require transplantation of cells embedded in appropriate biomaterial scaffolds. Such structures may consist of 3D non-woven fibrous materials whereas little is known about the impact of mesh size, pore architecture and fibre morphology on cellular behavior. In this study, we have developed polyvinylidene fluoride (PVDF) non-woven scaffolds with round, trilobal, or snowflake fibre cross section and different fibre crimp patterns (10, 16, or 28 needles per inch). Human mesenchymal stromal cells (MSCs) from adipose tissue were seeded in parallel on these scaffolds and their growth was compared. Initial cell adhesion during the seeding procedure was higher on non-wovens with round fibres than on those with snowflake or trilobal cross sections. All PVDF non-woven fabrics facilitated cell growth over a time course of 15 days. Interestingly, proliferation was significantly higher on non-wovens with round or trilobal fibres as compared to those with snowflake profile. Furthermore, proliferation increased in a wider, less dense network. Scanning electron microscopy (SEM) revealed that the MSCs aligned along the fibres and formed cellular layers spanning over the pores. 3D PVDF non-woven scaffolds support growth of MSCs, however fibre morphology and mesh size are relevant: proliferation is enhanced by round fibre cross sections and in rather wide-meshed scaffolds.
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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".