Magnetic microboats for floating, stiffness tunable, air–liquid interface epithelial cultures
Bibliographic record
Abstract
To study respiratory diseases, in vitro airway epithelial models are commonly implemented by culturing airway cells on a porous surface at an air-liquid interface (ALI). However, these surfaces are often supraphysiologically stiff, which is known to affect the organization, maturation, and responses of cells to potential therapies in other biological culture models. While it is possible to culture cells on soft hydrogel substrates at an air-liquid interface, these techniques are challenging to implement particularly in high-throughput applications which require robust and repetitive material handling procedures. To address these two limitations and characterize epithelial cultures on substrates of varying stiffness at the ALI, we developed a novel "lung-on-a-boat", in which stiffness-tuneable hydrogels are integrated into the bottoms of polymeric microstructures, which normally float at the air-liquid interface. An embedded magnetic material can be used to sink the boat on demand when a magnetic field is applied, enabling reliable transition between submerged and ALI culture. In this work, we prototype a functional ALI microboat platform, with integrated stiffness-tunable polyacrylamide hydrogel surfaces, and validate the use of this technology with a model epithelial cell line. We verify sufficient transport through the hydrogel base to maintain cell viability and stimulate cultures, using a model nanoparticle with known toxicity. We then demonstrate significant morphological and functional effects on epithelial barrier formation, suggesting that substrate stiffness is an important parameter to consider in the design of in vitro epithelial ALI models for drug discovery and fundamental research.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".