Microbiome dysbiosis occurred in hypertrophic scars is dominated by S. aureus colonization
Bibliographic record
Abstract
Background The mechanisms of hypertrophic scar formation and its tissue inflammation remain unknown. Methods We collected 33 hypertrophic scar (HS) and 36 normal skin (NS) tissues, and detected the tissue inflammation and bacteria using HE staining, Gram staining, and transmission electronic microscopy (TEM), in situ hybridization and immunohistochemistry for MCP-1, TNF-α, IL-6 and IL-8. In addition, the samples were assayed by 16S rRNA sequencing to investigate the microbiota diversity in HS, and the correlation between the microbiota and the indices of Vancouver Scar Scale(VSS)score. Results HE staining showed that a dramatically increased number of inflammatory cells accumulated in HS compared with NS, and an enhanced number of bacteria colonies was found in HS by Gram staining, even individual bacteria could be clearly observed by TEM. In situ hybridization demonstrated that the bacteria and inflammation cells co-localized in the HS tissues, and immunohistochemistry indicated the expression of MCP-1, TNF-α, IL-6, and IL-8 were significantly upregulated in HS than that in NS. In addition, there was a significantly different microbiota composition between HS and NS. At the phylum level, Firmicutes was significantly higher in HS than NS. At the genus level, S. aureus was the dominant species, which was significantly higher in HS than NS, and was strongly correlated with VSS indices. Conclusion Microbiome dysbiosis, dominated by S. aureus , occurred in HS formation, which is correlated with chronic inflammation and scar formation, targeting the microbiome dysbiosis is perhaps a supplementary way for future scar management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".