What is Slough? A pilot study to define the proteomic and microbial composition of wound slough and its implications for wound healing
Bibliographic record
Abstract
Abstract Slough is a well-known feature of non-healing wounds. This study aims to determine the proteomic and microbiologic components of slough as well as interrogate the associations between wound slough components and wound healing. Twenty-three subjects with slow-to-heal wounds and visible slough were enrolled. Etiologies included venous stasis ulcers, post-surgical site infections, and pressure ulcers. Patient co-morbidities and wound healing outcome at 3-months post-sample collection was recorded. Debrided slough was analyzed microscopically, through untargeted proteomics, and high-throughput bacterial 16S-ribosomal gene sequencing. Microscopic imaging revealed wound slough to be amorphous in structure and highly variable. 16S-profiling found slough microbial communities to associate with wound etiology and location on the body. Across all subjects, slough largely consisted of proteins involved in skin structure and formation, blood-clot formation, and immune processes. To predict variables associated with wound healing, protein, microbial, and clinical datasets were integrated into a supervised discriminant analysis. This analysis revealed that healing wounds were enriched for proteins involved in skin barrier development and negative regulation of immune responses. While wounds that deteriorated over time started off with a higher baseline Bates-Jensen Wound Assessment Score and were enriched for anerobic bacterial taxa and chronic inflammatory proteins. To our knowledge, this is the first study to integrate clinical, microbiome, and proteomic data to systematically characterize wound slough and integrate it into a single assessment to predict wound healing outcome. Collectively, our findings underscore how slough components can help identify wounds at risk of continued impaired healing and serves as an underutilized biomarker.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".