Microengineered diabetic wound-on-a-chip model for emulating chronic wound dynamics
Bibliographic record
Abstract
systems and animal models often fail to replicate the intricate cellular interactions and microenvironmental complexity of human diabetic wounds. To address this gap, a humanized 3D diabetic wound-on-a-chip (DWOC) model was developed to simulate key aspects of diabetic wound pathology under physiologically relevant conditions. This four-channel microfluidic platform integrates human dermal fibroblasts and macrophages within a collagen I matrix to mimic the dermis, alongside endothelial cells embedded in Matrigel to represent the vascular compartment. The system was subjected to hyperglycemic conditions with added advanced glycation end-products (AGEs) and lipopolysaccharide (LPS), alongside normoglycemic controls. Cellular viability, extracellular matrix (ECM) remodeling, myofibroblast differentiation, angiogenesis, and intercellular signaling were assessed using immunofluorescence markers (CD68, α-SMA, CD31, VE-cadherin, SLUG). Cytokine profiling (ELISA, multiplex assays) evaluated inflammatory responses. The DWOC effectively replicated hallmarks of diabetic wound pathology, including impaired ECM remodeling, disrupted dermal-vascular cell crosstalk, defective angiogenesis, and signs of endothelial-to-mesenchymal transition (EndMT) in endothelial cells under diabetic stress. Elevated pro-inflammatory markers (IL-1β, TNF-α, MMP9) and reduced anti-inflammatory/angiogenic factors (IL-10, VEGF-A) reflected the chronic inflammatory and angiogenic imbalance characteristic of non-healing diabetic ulcers. This advanced DWOC platform offers a physiologically relevant, human-specific model for studying diabetic wound healing, highlighting endothelial-to-mesenchymal transition as a critical pathological feature and enabling preclinical evaluation of targeted therapies.
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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 | 0.001 |
| 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 | 0.001 |
| 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".