Skin repairing procedure inspired polypyrrole/bacterial cellulose/platelet rich plasma composite hydrogel as diabetes wound dressing
Bibliographic record
Abstract
Diabetic ulcers represent a kind of severe chronic wound that presents significant challenges to global healthcare systems. The impaired healing process in diabetic wounds is attributed to persistent inflammation, compromised angiogenesis, and bacterial infections. When dermic injury occurs, the skin will initiate a complex cascade of natural repair procedures to facilitate coordinated progression through inflammatory, proliferative, and remodelling phases. Inspired by these physiological repair processes, a multifunctional hydrogel dressing was designed by combining capacitive polypyrrole (PPy) with bacterial cellulose (BC) hydrogel and platelet-rich plasma (PRP) to achieve synergistic antibacterial efficacy, immunomodulation of the wound microenvironment, and enhanced tissue regeneration. The BC hydrogel serves as a scaffold for PRP encapsulation, protecting growth factors from protease degradation while enabling sustained release. Capacitive PPy not only provides potent antibacterial activity through electric field (EF) mediated charge storage, but also promotes electrical signal transduction to regulate growth factor release kinetics. In vitro results revealed that pre-charged polypyrrole/bacterial cellulose/platelet-rich plasma (PBP) composite hydrogels exhibited superior bactericidal efficacy, enhanced fibroblast and endothelial cell proliferation, and modulation of macrophage polarization. In diabetic wound models, treatment with the electroactivated PBP composite hydrogel demonstrated a marked reduction in inflammatory responses, accelerated vascular regeneration, enhanced collagen deposition, and overall improvement in wound healing. Inspired by the physiological skin repair process, we developed a multifunctional hydrogel dressing by integrating a bacterial cellulose fiber network with capacitive polypyrrole and platelet-rich plasma. This hydrogel system effectively shields growth factors from proteolytic degradation while enabling their sustained release. The incorporation of capacitive polypyrrole not only imparts potent antibacterial properties through electric field-mediated charge storage but also facilitates electrical signal transduction to modulate growth factor release kinetics. Furthermore, the hydrogel actively regulates the immune microenvironment of diabetic wounds, enhances revascularization, and significantly accelerates wound healing. This innovative strategy demonstrates substantial potential for advancing therapeutic approaches in diabetic wound management.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".