Advancing injectable hydrogels for wound treatment: targeted control of oxidative stress toward personalized regeneration
Bibliographic record
Abstract
Wound injuries, including severe burns, diabetic foot ulcers, and chronic skin defects, remain a significant clinical burden due to their complexity, susceptibility to infection, and impaired healing, particularly in elderly individuals and patients with diabetes or vascular diseases. In these conditions, the wound healing process is disrupted by excessive oxidative stress, persistent inflammation, and microbial infection, ultimately leading to impaired tissue regeneration. These challenges highlight the urgent need for advanced wound care strategies capable of actively modulating the wound microenvironment to facilitate effective and timely healing. Among various hydrogel systems, injectable horseradish peroxidase (HRP)-catalyzed hydrogels have gained attention due to their biocompatibility, ease of application, tunable properties, ability to fill irregular wound geometries, versatility in material selection, and mild crosslinking conditions. These features make them promising candidates for multifunctional wound dressings in both acute and chronic wound management. This review provides a comprehensive overview of recent advancements in the development of injectable HRP-catalyzed hydrogels for wound treatment. We highlight key design strategies that confer multifunctional therapeutic capabilities, including hemostatic function, antibacterial activity, and reactive oxygen species-releasing and scavenging properties. Particular emphasis is placed on the incorporation of gasotransmitter-releasing components to regulate the wound microenvironment effectively. Furthermore, we discuss emerging strategies aimed at transforming these hydrogels into smart wound dressings with advanced functionalities, such as oxygen-releasing ability, electrical conductivity, and microbiome-modulating features. Finally, we emphasize the importance of developing scalable, safe, and personalized hydrogel systems capable of addressing the complex pathophysiology of chronic wounds and improving patient-specific wound care outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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".