Bio-MOF-Polymeric Hybrid Wound Healing Patch for Enhanced Transdermal Codelivery of Curcumin and Heparin
Bibliographic record
Abstract
Abstract Modern healthcare engineering demands wound dressings with high biocompatibility and robust mechanical strength that enable sustained codrug delivery for acute injuries, effectively controlling infections without side effects. In this study, a unique cryo patch was developed by integrating two biocompatible polymers-poly(vinyl alcohol) (PVA) and poly(ethylene glycol) (PEG) in an 8:2 ratio─along with curcumin and heparin encapsulated within the pores of the biocompatible metal–organic framework, zinc l-glutamate (Zn-GA), a well-established bio-MOF. This cryo patch exhibited several superior properties compared to existing wound dressings, including remarkable mechanical durability (13.8 N), an enhanced water swelling ratio (231.1%), antifreezing capability (−46 °C), adhesiveness, self-healing properties, and a substantial drug-loading capacity (approximately 45,000 times more curcumin than water). It also demonstrated sustained drug release, hemocompatibility (hemolysis levels below 2% even at high concentrations of up to 40 mg/mL), and effective antibacterial, antifungal, and antioxidant activities. In vitro studies showed excellent cell viability in HaCaT cells, with the curcumin/heparin@patch significantly promoting wound closure during scratch assays. At a low dose of 250 μg/mL, the area occupied by migrated cells increased by 1.5- and 1.6-times compared to the control and drug-free patch, respectively, after 48 h. Furthermore, the cryo patch displayed potential anti-inflammatory properties. These findings highlight the cryo patch’s unique combination of high drug-loading capacity, enhanced wound healing at low drug concentrations, and versatile biocompatibility, making it a promising candidate for advanced wound dressing applications.
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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) | 0.001 | 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".