Revisiting tissue tensegrity: Biomaterial-based approaches to measure forces across length scales
Bibliographic record
Abstract
Cell-generated forces play a foundational role in tissue dynamics and homeostasis and are critically important in several biological processes, including cell migration, wound healing, morphogenesis, and cancer metastasis. Quantifying such forces in vivo is technically challenging and requires novel strategies that capture mechanical information across molecular, cellular, and tissue length scales, while allowing these studies to be performed in physiologically realistic biological models. Advanced biomaterials can be designed to non-destructively measure these stresses in vitro, and here, we review mechanical characterizations and force-sensing biomaterial-based technologies to provide insight into the mechanical nature of tissue processes. We specifically and uniquely focus on the use of these techniques to identify characteristics of cell and tissue “tensegrity:” the hierarchical and modular interplay between tension and compression that provide biological tissues with remarkable mechanical properties and behaviors. Based on these observed patterns, we highlight and discuss the emerging role of tensegrity at multiple length scales in tissue dynamics from homeostasis, to morphogenesis, to pathological dysfunction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".