Multiple dynamic crosslinked multifunctional hydrogels with glucose/pH dual-responsive adipose-derived stem cells-exosomes-releasing for diabetic wound healing
Bibliographic record
Abstract
Background: Diabetic wounds feature a high-glucose and acidic microenvironment that impairs macrophage polarization and healing. Adipose-derived stem cell-derived exosomes (ADSC-exos) show therapeutic potential but suffer from rapid clearance. This study aimed to develop a smart hydrogel for glucose/pH-responsive ADSC-exos release. Methods: A dual-responsive hydrogel (HAP/OCS/PEG/Ag-E) was fabricated via dynamic triple cross-linking. Characterization included rheometry, mechanical tests, and microscopy. In vitro macrophage polarization was assessed via flow cytometry and Western blot. A diabetic mouse wound model evaluated healing rates, histology, angiogenesis, and inflammation. Proteomics and pathway inhibition studies explored mechanisms. Statistical analysis used t-tests and ANOVA. Results: The hydrogel exhibited excellent self-healing, adhesion, and controlled ADSC-exos release under high-glucose/acidic conditions. It promoted M2 macrophage polarization, reduced pro-inflammatory cytokines (IL-1β, IL-6, TNF-α), and accelerated wound healing with enhanced angiogenesis and collagen deposition. Mechanistically, the hydrogel suppressed the Notch/NF-κB/NLRP3 signaling pathway. Conclusion: The smart hydrogel facilitates diabetic wound healing through microenvironment-responsive ADSC-exos release and Notch/NF-κB/NLRP3 pathway inhibition, offering a promising strategy for chronic wound treatment.
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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".