Fabrication of Nanofibrous PVA/Alginate‐Sulfate Substrates for Growth Factor Delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it