Engineered Robust Hydrophobic/Hydrophilic Nanofibrous Scaffolds with Drug-Eluting, Antioxidant, and Antimicrobial Capacity
Bibliographic record
Abstract
Multifunctional nanofibrous architectures have attracted extensive attention for biomedical applications due to their adjustable and versatile properties. Electrospun fabrics stand out as key building blocks for these structures, yet improving their mechanobiological and physicochemical performance is a challenge. Here, we introduce biodegradable engineered hydrophobic/hydrophilic scaffolds consisting of electrospun polylactide nanofibers coated with drug-eluting synthetic (poly(vinyl alcohol)) and natural (starch) polymers. The microstructure of these composite scaffolds was tailored for an increased hydrophilicity, optimized permeability, water retention capacity of up to 5.1 g/g, and enhanced mechanical properties under both dry and wet conditions. Regarding the latter, normalized tensile strengths of up to 32.4 MPa were achieved thanks to the improved fiber interactions and fiber-coating stress transfer. Curcumin was employed as a model drug, and its sustained release in a pure aqueous medium was investigated for 35 days. An in-depth study of the release kinetics revealed the outstanding water solubility and bioavailability of curcumin, owing to its complexation with the hydrophilic polymers and further delineated the role of the hydrophobic nanofibrous network in regulating its release rate. The modified curcumin endowed the composites with antioxidant activities up to 5.7 times higher than that of free curcumin as well as promising anti-inflammatory and bacteriostatic activities. The cytocompatibility and cell proliferation capability on human dermal fibroblasts also evidenced the safe use of the constructs. Finally, the fabrics present pH-responsive color-changing behavior easily distinguishable within the pH range of 5-9. Thus, these designs offer a facile and cost-effective roadmap for the fabrication of smart multifunctional biomaterials, especially for chronic wound healing.
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.
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".