Microfabricated arrays for high-throughput screening of cellular response to cyclic substrate deformation
Bibliographic record
Abstract
Mechanical forces play an important role in regulating cellular function and have been shown to modulate cellular response to other factors in the cellular microenvironment. Presently, no technique exists to rapidly screen for the effects of a range of uniform mechanical forces on cellular function. In this work, we developed and characterized a novel microfabricated array capable of simultaneously applying cyclic equibiaxial substrate strains ranging in magnitude from 2 to 15% to small populations of adherent cells. The array is versatile, and capable of simultaneously generating a range of substrate strain fields and magnitudes. The design can be extended to combinatorially manipulate other mechanobiological culture parameters in the cellular microenvironment. As a first demonstration of this technology, the array was used to determine the effects of equibiaxial mechanical strain on activation of the canonical Wnt/beta-catenin signaling pathway in cardiac valve mesenchymal progenitor cells. This high-throughput approach to mechanobiological screening enabled the identification of a novel co-dependence between strain magnitude and duration of stimulation in controlling beta-catenin nuclear accumulation. More generally, this versatile platform has broad applicability in the fields of mechanobiology, tissue engineering and pathobiology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".