E‐FLOAT: Extractable Floating Liquid Gel‐Based Organ‐on‐a‐Chip for Airway Tissue Modeling under Airflow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 |
| 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) | 0.000 | 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 it