Comparison of cell behavior on pva/pva-gelatin electrospun nanofibers with random and aligned configuration
Bibliographic record
Abstract
Electrospinning technique is able to create nanofibers with specific orientation. Poly(vinyl alcohol) (PVA) have good mechanical stability but poor cell adhesion property due to the low affinity of protein. In this paper, extracellular matrix, gelatin is incorporated into PVA solution to form electrospun PVA-gelatin nanofibers membrane. Both randomly oriented and aligned nanofibers are used to investigate the topography-induced behavior of fibroblasts. Surface morphology of the fibers is studied by optical microscopy and scanning electron microscopy (SEM) coupled with image analysis. Functional group composition in PVA or PVA-gelatin is investigated by Fourier Transform Infrared (FTIR). The morphological changes, surface coverage, viability and proliferation of fibroblasts influenced by PVA and PVA-gelatin nanofibers with randomly orientated or aligned configuration are systematically compared. Fibroblasts growing on PVA-gelatin fibers show significantly larger projected areas as compared with those cultivated on PVA fibers which p-value is smaller than 0.005. Cells on PVA-gelatin aligned fibers stretch out extensively and their intracellular stress fiber pull nucleus to deform. Results suggest that instead of the anisotropic topology within the scaffold trigger the preferential orientation of cells, the adhesion of cell membrane to gelatin have substantial influence on cellular behavior.
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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".