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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".