DNA methylation analysis of negative pressure therapy effect in diabetic foot ulcers
Bibliographic record
Abstract
OBJECTIVE: Negative pressure wound therapy (NPWT) has been used to treat diabetic foot ulcerations (DFUs). Its action on the molecular level, however, is only partially understood. Some earlier data suggested NPWT may be mediated through modification of local gene expression. As methylation is a key epigenetic regulatory mechanism of gene expression, we assessed the effect of NPWT on its profile in patients with type 2 diabetes (T2DM) and neuropathic non-infected DFUs. METHODS: Of 36 included patients, 23 were assigned to NPWT and 13 to standard therapy. Due to ethical concerns, the assignment was non-randomized and based on wound characteristics. Tissue samples were obtained before and 8 ± 1 days after therapy initiation. DNA methylation patterns were checked by Illumina Methylation EPIC kit. RESULTS: In terms of clinical characteristics, the groups presented typical features of T2DM; however, the NPWT group had significantly greater wound area: 16.8 cm2 vs 1.4 cm2 (P = 0.0003). Initially only one region at chromosome 5 was differentially methylated. After treatment, 57 differentially methylated genes were found, mainly located on chromosomes 6 (chr6p21) and 20 (chr20p13); they were associated with DNA repair and autocrine signaling via retinoic acid receptor. We performed differential analyses pre treatment and post treatment. The analysis revealed 426 differentially methylated regions in the NPWT group, but none in the control group. The enrichment analysis showed 11 processes significantly associated with NPWT, of which 4 were linked with complement system activation. All but one were hypermethylated after NPWT. CONCLUSION: The NPWT effect on DFUs may be mediated through epigenetic changes resulting in the inhibition of complement system activation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".