Fabrication of Nanofibrous PVA/Alginate‐Sulfate Substrates for Growth Factor Delivery
Bibliographic record
Abstract
Providing affinity sites on alginate (ALG) matrix enables specific binding of growth factors to the polymer backbone and allows their release in a controlled fashion. In this study, we used a blend of alginate sulfate (ALG-S) and polyvinyl alcohol (PVA) to fabricate electrospun scaffolds capable of delivering a heparin-like growth factor, transforming growth factor-beta1 (TGF-β1). The alginate was sulfated with different degrees of sulfation (DS, from 0.8, 3.4 to 12.4%) by a simple process. The success of sulfation was determined by Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), elemental analysis, ultraviolet (UV) spectroscopy and staining with dimethylmethylene blue. The physical-mechanical properties of nanofibrous mats were characterized by scanning electron microscopy (SEM), FTIR, energy-dispersive X-ray spectroscopy (EDX), tensile strength and mass loss analysis. Additionally, the release kinetics of transforming growth factor-β1 (TGF-β1) from PVA/ALG-S and PVA/ALG scaffolds were compared. The results showed that the binding and entrapment of TGF-β1 to the nanofibrous scaffolds are improved by the addition of sulfate group to alginate. In conclusion, our results support that nanofibrous scaffold based on PVA/ALG-S can deliver growth factors in tissue engineering application. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 107A: 403-413, 2019.
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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".