Loading and Release of Quercetin from Contact-Drawn Polyvinyl Alcohol Fiber Scaffolds
Bibliographic record
Abstract
Polymeric drug releasing systems have numerous applications for the treatment of chronic diseases and traumatic injuries. In this study, a simple, cost-effective, and scalable method for dry spinning of crosslinked polyvinyl alcohol (PVA) fibers is presented. This method utilizes an entangled solution of PVA to form liquid bridges that are drawn into rapidly drying fibers through extensional flow. The fibers are crosslinked by a one-pot reaction in which glyoxal is introduced to the PVA solution prior to contact drawing. Failure analysis of fiber formation is used to understand the interplay of polymer concentration, glyoxal concentration, and crosslinking time to identify appropriate formulations for the production of glyoxal-crosslinked PVA fibers. The small molecule quercetin (an anti-inflammatory plant flavonoid) can be added to the one-pot reaction and is shown to be incorporated into the fibers in a concentration-dependent manner. Upon rehydration in an aqueous medium, the glyoxal-crosslinked PVA fiber scaffolds retain their morphology and slowly degrade, as measured over the course of 10 days. As the scaffolds degrade, they release the loaded quercetin, reaching a cumulative release of 56 ± 6% of the loaded drug after 10 days. The bioactivity of the released quercetin is verified by combining quercetin-loaded fibers with contact-drawn polyethylene oxide-type I collagen (PEO-Col) fibers and monitoring the growth of PC12 cells on the fibers. PC12 cells readily attach to the PEO-Col fibers and display increased nerve growth factor-induced elongation and neurite formation in the presence of quercetin-loaded PVA fibers relative to substrates formed from only PEO-Col fibers or PEO-Col and PVA fibers without quercetin.
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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".