High Throughput Screening of Cell Mechanical Response Using a Stretchable 3D Cellular Microarray Platform
Bibliographic record
Abstract
Cells in vivo are constantly subjected to multiple microenvironmental mechanical stimuli that regulate cell function. Although 2D cell responses to the mechanical stimulation have been established, these methods lack relevance as physiological cell microenvironments are in 3D. Moreover, the existing platforms developed for studying the cell responses to mechanical cues in 3D either offer low-throughput, involve complex fabrication, or do not allow combinatorial analysis of multiple cues. Considering this, a stretchable high-throughput (HT) 3D cell microarray platform is presented that can apply dynamic mechanical strain to cells encapsulated in arrayed 3D microgels. The platform uses inkjet-bioprinting technique for printing cell-laden gelatin methacrylate (GelMA) microgel array on an elastic composite substrate that is periodically stretched. The developed platform is highly biocompatible and transfers the applied strain from the stretched substrate to the cells. The HT analysis is conducted to analyze cell mechano-responses throughout the printed microgel array. Also, the combinatorial analysis of distinct cell behaviors is conducted for different GelMA microenvironmental stiffnesses in addition to the dynamic stretch. Considering its throughput and flexibility, the developed platform can readily be scaled up to introduce a wide range of microenvironmental cues and to screen the cell responses in a HT way.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".