Single-Cell Analysis Reveals Cxcl14 <sup>+</sup> Fibroblast Accumulation in Regenerating Diabetic Wounds Treated by Hydrogel-Delivering Carbon Monoxide
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Nonhealing skin wounds are a problematic complication associated with diabetes. Therapeutic gases delivered by biomaterials have demonstrated powerful wound healing capabilities. However, the cellular responses and heterogeneity in the skin regeneration process after gas therapy remain elusive. Here, we display the benefit of the carbon monoxide (CO)-releasing hyaluronan hydrogel (CO@HAG) in promoting diabetic wound healing and investigate the cellular responses through single-cell transcriptomic analysis. The presented CO@HAG demonstrates wound microenvironment responsive gas releasing properties and accelerates the diabetic wound healing process in vivo . It is found that a new cluster of Cxcl14 + fibroblasts with progenitor property is accumulated in the CO@HAG-treated wound. This cluster of Cxcl14 + fibroblasts is yet unreported in the skin regeneration process. CO@HAG-treated wound macrophages feature a decrease in pro-inflammatory property, while their anti-inflammatory property increases. Moreover, the TGF-β signal between the pro-inflammatory (M1) macrophage and the Cxcl14 + fibroblast in the CO@HAG-treated wound is attenuated based on cell–cell interaction analysis. Our study provides a useful hydrogel-mediated gas therapy method for diabetic wounds and new insights into cellular events in the skin regeneration process after gas-releasing biomaterials therapy.
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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 | 0.000 | 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) | 0.001 | 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".