Bibliographic record
Abstract
It has become increasingly appreciated that living mammalian cells are not just complex biochemical reactors but also sophisticated biomechanical systems that can adapt their mechanical properties to various signals and perturbations from the extracellular space, and integrate with intracellular signaling events through a process called mechanotransduction, to regulate cell behaviors. To gain fundamental insights into such biomechanical nature of mammalian cells, many biomechanical tools have been developed with unprecedented spatiotemporal resolutions covering both molecular and cellular length scales. In this chapter, we describe a recently developed biomechanical tool, termed “stretchable micropost array cytometry” (SMAC), which is capable of quantitative control and real-time measurements of both mechanical stimuli and cellular biomechanical responses with a high spatiotemporal subcellular resolution. We further discuss implementations of the SMAC for characterizing cell cytoskeletal contractile force, cell stiffness, and cell adhesion signaling and dynamics at both whole-cell and subcellular scales in real time. We conclude with remarks regarding future improvements and applications of the SMAC for cell mechanics and mechanobiology studies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".