Development of pH-responsive hydrogel films encapsulated with PEG-VEGF165 bioconjugates for wound dressings
Bibliographic record
Abstract
Protein-based wound dressings have emerged as a topic of interest in chronic wound healing owing to their distinct physical, chemical, and biological characteristics. Growth factor proteins, such as platelet-derived growth factor and vascular endothelial growth factor (VEGF), play a pivotal role in wound healing by mediating angiogenic responses and promoting the formation of new blood vessels, thereby accelerating recovery. However, protein delivery faces several challenges that can be addressed through the bioconjugation of proteins with macromolecules, which enhances their stability, solubility, bioactivity, and half-life. Over the years, various chemical strategies have been developed to conjugate synthetic polymers onto proteins effectively. One such approach is the "grafting to" strategy, which involves the covalent attachment of pre-formed poly(ethylene glycol) (PEG) to target molecules like proteins or other macromolecules. This method improves the solubility, stability, and bioavailability of the target molecules. Various formulations including foams, fibers, and hydrogel films have been explored for safe delivery of proteins and protein bioconjugates. Among these, polymeric hydrogel films have gained significant attention due to their non-cytotoxic nature, versatility, biocompatibility, and ability to provide a moist environment conducive to healing. My MSc research project focuses on developing hydrogel films crosslinked with boronic ester bonds that encapsulate PEG-VEGF165 bioconjugates to promote the healing process in chronic wounds. The bioconjugates were characterized using techniques such as gel electrophoresis and dynamic light scattering. Additionally, hydrogel films were fabricated with biocompatible poly(vinyl alcohol) (PVA) crosslinked with tetrahydroxydiboronic (THDB), a diboronic acid crosslinker, ensuring dimensional stability and effective encapsulation of the bioconjugates. These hydrogels degraded in response to acidic and alkali pHs, hydrogen peroxide, and glucose, which could be found in wounds, leading to enhanced release of encapsulated PEG-VEGF bioconjugates. These results, combined with antimicrobial properties, suggest that the developed THDB-PVA/bioconjugate crosslinked films possess great potential for designing dermal wound healing systems.
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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".