Impact of elastic and inelastic substrate behaviors on mechanosensation
Bibliographic record
Abstract
In this review we summarize current data on the mechanics of synthetic and naturally occurring biopolymers that are routinely employed in examination of contractility and cellular mechanosensation. We discuss the effect of physical boundaries on the mechanical behaviors of cell substrates and cellular mechanosensation. The application of contractile forces to underlying substrates enables anchorage-dependent cells to probe the physical properties of their microenvironment. Compliant substrates deform as a result of contractile forces generated by adherent cells and, in turn, the mechanical response of substrates influences numerous cellular processes. Unlike synthetic polymers that exhibit linear elastic responses to forces applied by adherent cells, naturally-occurring biopolymers exhibit non-linear, viscoelastic behavior. In turn, the viscoelastic behavior of fibrillar biopolymers may contribute to irreversible network compaction after application of cell-derived forces. Comprehensive characterization of the unusual mechanical properties of extracellular matrix proteins like collagen has provided novel insights into cell contractility and mechanosensation. We suggest that in the future, fabrication and application of novel substrates with fibrillar structures and non-linear viscoelastic behavior will be needed for a better understanding of the role of mechanosensation in many physiological and pathological processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".