Engineering a Multilayer Microfluidic Airway-On-A-Chip with Tunable GelMA Hydrogel for Physiologically Relevant Aerosol Exposure Studies
Bibliographic record
Abstract
Abstract Climate change-driven increases in forest fires pose a major global health risk due to exposure to smoke containing hazardous gases and fine particulates, emphasizing the need for physiologically relevant in vitro airway models for studying smoke-induced responses. Microfluidic lung-on-a-chip technologies provide a strong foundation for in vitro airway modeling and ongoing developments are expanding their ability to incorporate multicellular organization, extracellular matrix complexity, and physiologically relevant exposure methods. This work presents the optimization and integration of a photopolymerizable gelatin methacrylate (GelMA)-based hydrogel into a microfluidic airway-on-a-chip that models the human small conducting airways and supports controlled aerosol exposure to wood smoke. The GelMA hydrogel was optimized to support fibroblast encapsulation, endothelial, and epithelial adhesion and robust mechanical stability. The device combines the hydrogel with a compartmentalized microchannel layout, and sacrificial molding to create a 3D organotypic airway culture featuring a multilayer architecture, 3D stromal matrix, and a perfusable vasculature-like lumen. Coupling the platform with a custom aerosol exposure system enables precise, biomimetic exposure to whole wood smoke. Proof-of-concept studies using transforming growth factor beta1 (TGF-β1) and whole wood smoke elicited expected inflammatory and fibrotic responses, validating the platform’s physiological relevance for inhalation studies and investigating smoke-induced airway remodeling and inflammation.
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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".