Analytical methods in studying cell force sensing: principles, current technologies and perspectives
Bibliographic record
Abstract
Mechanical stimulation plays a crucial role in numerous biological activities, including tissue development, regeneration and remodeling. Understanding how cells respond to their mechanical microenvironment is vital for investigating mechanotransduction with adequate spatial and temporal resolution. Cell force sensing-also known as mechanosensation or mechanotransduction-involves force transmission through the cytoskeleton and mechanochemical signaling. Insights into cell-extracellular matrix interactions and mechanotransduction are particularly relevant for guiding biomaterial design in tissue engineering. To establish a foundation for mechanical biomedicine, this review will provide a comprehensive overview of cell mechanotransduction mechanisms, including the structural components essential for effective mechanical responses, such as cytoskeletal elements, force-sensitive ion channels, membrane receptors and key signaling pathways. It will also discuss the clutch model in force transmission, the role of mechanotransduction in both physiology and pathological contexts, and biomechanics and biomaterial design. Additionally, we outline analytical approaches for characterizing forces at cellular and subcellular levels, discussing the advantages and limitations of each method to aid researchers in selecting appropriate techniques. Finally, we summarize recent advancements in cell force sensing and identify key challenges for future research. Overall, this review should contribute to biomedical engineering by supporting the design of biomaterials that integrate precise mechanical information.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".