The CaT stretcher: An open-source system for delivering uniaxial strain to cells and tissues (CaT)
Bibliographic record
Abstract
Integration of mechanical cues in conventional 2D or 3D cell culture platforms is an important consideration for in vivo and ex vivo models of lung health and disease. Available commercial and published custom-made devices are frequently limited in breadth of applications, scalability, and customization. Herein we present a technical report on an open-source, cell and tissue (CaT) stretcher, with modularity for different in vitro and ex vivo systems, that includes the following features: 1) Programmability for modeling different breathing patterns, 2) scalability to support low to high-throughput experimentation, and 3) modularity for submerged cell culture, organ-on-chips, hydrogels, and live tissues. The strategy for connecting the experimental cell or tissue samples to the stretching device were designed to ensure that traditional biomedical outcome measurements including, but not limited to microscopy, soluble mediator measurement, and gene and protein expression remained possible. Lastly, to increase the uptake of the device within the community, the system was built with economically feasible and available components. To accommodate diverse in vitro and ex vivo model systems we developed a variety of chips made of compliant polydimethylsiloxane (PDMS) and optimized coating strategies to increase cell adherence and viability during stretch. The CaT stretcher was validated for studying mechanotransduction pathways in lung cells and tissues, with an increase in alpha smooth muscle actin protein following stretch for 24 h observed in independent submerged monolayer, 3D hydrogel, and live lung tissue experiments. We anticipate that the open-source CaT stretcher design will increase accessibility to studies of the dynamic lung microenvironment through direct implementation by other research groups or custom iterations on our designs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".