Novel mediators regulating angiogenesis in diabetic foot ulcer healing
Bibliographic record
Abstract
A non-healing diabetic foot ulcer (DFU) is a debilitating clinical problem amounting to socioeconomic and psychosocial burdens. DFUs increase morbidity due to prolonged treatment and mortality in the case of non-treatable ulcers resulting in gangrene and septicemia. The overall amputation rate of the lower extremity with DFU ranges from 3.34% to 42.83%. Wound debridement, antibiotics, applying growth factors, negative pressure wound therapy, hyperbaric oxygen therapy, topical oxygen, and skin grafts are common therapies for DFU. However, recurrence and nonhealing ulcers are still major issues. Chronicity of inflammation, hypoxic environment, poor angiogenesis, and decreased formation of the extracellular matrix (ECM) are common impediments leading to nonhealing patterns of DFUs. Angiogenesis is crucial for wound healing since proper vessel formation facilitates nutrients, oxygen, and immune cells to the ulcer tissue to help in clearing out debris and facilitate healing. However, poor angiogenesis due to decreased expression of angiogenic mediators and matrix formation results in nonhealing and ultimately amputation. Multiple proangiogenic mediators and vascular endothelial growth factor (VEGF) therapy exist to enhance angiogenesis, but the results are not satisfactory. Thus, there is a need to investigate novel pro-angiogenic mediators that can either alone or in combination enhance the angiogenesis and healing of DFUs. In this article, we critically reviewed the existing pro-angiogenic mediators followed by potentially novel factors that might play a regulatory role in promoting angiogenesis and wound healing in DFUs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".