Wound pH-Modulating Strategies for Diabetic Wound Healing
Bibliographic record
Abstract
Significance:Chronic diabetic wounds on the lower extremities (diabetic foot ulcers, DFU) are one of the most prevalent and life-threatening complications of diabetes, responsible for significant loss of quality of life and cost to the health care system. Available pharmacologic treatments fail to achieve complete healing in many patients. Recent studies and investigational treatments have highlighted the potential of modulating wound pH in DFU. Recent Advances:Data from in vitro, preclinical, and clinical studies highlight the role of pH in the pathophysiology of DFU, and topical administration of pH-lowering agents have shown promise as a therapeutic strategy for diabetic wounds. In this critical review, we describe the role of pH in DFU pathophysiology and present selected low-molecular-weight and hydrogel-based pH-modulating systems for wound healing and infection control in diabetic wounds. Critical Issues:The molecular mechanisms leading to pH alterations in diabetic wounds are complex and may differ between in vitro models, animal models of diabetes, and the human pathophysiology. Wound pH-lowering bandages for DFU therapy must be tested in established animal models of diabetic wound healing and patients with diabetes to establish a comprehensive benefit-risk profile. Future Directions:As our understanding of the role of pH in the pathophysiology of diabetic wounds is deepening, new treatments for this therapeutic target are being developed and will be tested in preclinical and clinical studies. These therapeutic systems will establish a target product profile for pH-lowering treatments such as an optimal pH profile for each wound healing stage. Thus, controlling wound bed pH could become a powerful tool to accelerate chronic diabetic wound healing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".