Force nanoscopy of cell mechanics and cell adhesion
Bibliographic record
Abstract
Cells are constantly exposed to mechanical stimuli in their environment and have several evolved mechanisms to sense and respond to these cues. It is becoming increasingly recognized that many cell types, from bacteria to mammalian cells, possess a diverse set of proteins to translate mechanical cues into biochemical signalling and to mediate cell surface interactions such as cell adhesion. Moreover, the mechanical properties of cells are involved in regulating cell function as well as serving as indicators of disease states. Importantly, the recent development of biophysical tools and nanoscale methods has facilitated a deeper understanding of the role that physical forces play in modulating cell mechanics and cell adhesion. Here, we discuss how atomic force microscopy (AFM) has recently been used to investigate cell mechanics and cell adhesion at the single-cell and single-molecule levels. This knowledge is critical to our understanding of the molecular mechanisms that govern mechanosensing, mechanotransduction, and mechanoresponse in living cells. While pushing living cells with the AFM tip provides a means to quantify their mechanical properties and examine their response to nanoscale forces, pulling single surface proteins with a functionalized tip allows one to understand their role in sensing and adhesion. The combination of these nanoscale techniques with modern molecular biology approaches, genetic engineering and optical microscopies provides a powerful platform for understanding the sophisticated functions of the cell surface machinery, and its role in the onset and progression of complex diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".