Force nanoscopy of cell mechanics and cell adhesion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it