E‐FLOAT: Extractable Floating Liquid Gel‐Based Organ‐on‐a‐Chip for Airway Tissue Modeling under Airflow
Bibliographic record
Abstract
Abstract Microfluidic lung‐on‐a‐chip systems are increasingly attractive tools for studying lung physiology and function because of their ability to accurately recapitulate spatiotemporal features of the airway tissue microenvironment including cellular organization, tissue architecture, and mechanical cues such as cyclic stretching and airflow. However, most lung‐on‐a‐chip devices to date rely on integrated design elements like membranes for airway cell culture, and focus mainly on enabling on‐chip monitoring and analysis while neglecting the need for off‐chip analysis. Here, an extractable floating liquid‐gel‐based organ‐on‐a‐chip for airway tissue modeling referred to as “E‐FLOAT” is described that is arrayable, scalable, and uniquely amenable to withstand physiologic airflow by microanchors. It is shown that E‐FLOAT can be combined with a custom airflow system that permits controlled injection of particulate matter for air pollution studies. Results show that airflow is critical to efficiently achieving physiologic mimicry of airway epithelium composition, tight junction expression, mucus production, and cilia formation on epithelial cells. It is also shown that E‐FLOAT allows standard on‐chip analysis while permitting complete sample extraction and off‐chip analysis via immunocytochemistry, microscopy, and histological sectioning and staining, thereby expanding the number and types of biological assays that can be used and questions that can be tackled.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".