Exploring the Therapeutic Potential of Remote Cold Atmospheric Plasma Jet in Diabetic Foot Ulcer Management: A Novel Clinical Case Report
Bibliographic record
Abstract
Diabetic foot ulcers (DFUs) present a formidable threat to individuals grappling with diabetes mellitus (DM), often culminating in severe complications like infection, gangrene, and the prospect of amputation. This study investigates the potential of cold atmospheric plasma (CAP) as an innovative therapeutic intervention to augment the recovery process in chronic DFUs. Examining a 67-year-old diabetic patient with a grade 3 DFU, the efficacy of CAP is inspected through an inventive treatment protocol. The research reveals a noteworthy decrease in both wound depth and bacterial load by the application of the remote CAP treatment, showcasing promising outcomes. The paper involvedly explores the diverse effects of remote CAP treatment, encompassing the generation of reactive species, electromagnetic fields, and ultraviolet (UV) light, which collectively initiate processes such as tissue regeneration, angiogenesis, and the inactivation of bacteria. While underscoring the safety and precision of the experimental procedure, the study underscores CAP's potential as a cost-effective and efficacious solution for DFU management, addressing a critical necessity in the global healthcare landscape. The findings provide valuable insights into the evolving realm of DFU treatment, emphasizing CAP's synergistic role in fostering healing and combatting infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".