Negative pressure wound therapy use in diabetic foot syndrome—from mechanisms of action to clinical practice
Bibliographic record
Abstract
BACKGROUND: Diabetes and its complications constitute a rising medical challenge. Special attention should be given to diabetic foot syndrome (DFS) due to its high rate of associated amputation and mortality. Negative pressure wound therapy (NPWT) is a frequently used supportive modality in a diabetic foot with ulcerations (DFUs). DESIGN: Here, we reviewed the current knowledge concerning the tissue and molecular mechanisms of NPWT action with an emphasis on diabetes research followed by a summary of clinical DFU studies and practice guidelines. RESULTS: Negative pressure wound therapy action results in two types of tissue deformations-macrodeformation, such as wound contraction, and microdeformation occurring at microscopic level. Both of them stimulate a wound healing cascade including tissue granulation promotion, vessel proliferation, neoangiogenesis, epithelialization and excess extracellular fluid removal. On the molecular level, NPWT results in an alteration towards more pro-angiogenic and anti-inflammatory conditions. It increases expression of several key growth factors, including vascular endothelial growth factor and fibroblast growth factor 2, while expression of inflammatory cytokinesis reduced. The NPWT application also alters the presence and function of matrix metalloproteinases. Clinical studies in DFU patients showed a superiority of NPWT over standard therapy in terms of efficacy outcomes, primarily wound healing and amputation rate, without a rise in adverse events. International guidelines point to NPWT as an important adjuvant therapy in DFU whose use is expected to increase. CONCLUSIONS: This current knowledge improves our understanding of NPWT action and its tailoring for application in diabetic patients. It may inform the development of new treatments for DFU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".