Measuring integrated cellular mechanical stress response at focal adhesions by optical tweezers
Bibliographic record
Abstract
The ability of cells to sustain mechanical stress is largely modulated by the cytoskeleton. We present a new application of optical tweezers to study cell's mechanical properties. We trap a fibronectin-coated bead attached to an adherent H4II-EC3 rat hepatoma cell in order to apply the force to the cell surface membrane. The bead position corresponding to the cell's local mechanical response at focal adhesions is measured with a quadrant detector. We assessed the cell response by tracking the evolution of the equilibrium force for 40 cells selected at random and selected a temporal window to assess the cell initial force expression at focal adhesions. The mean value of the force within this time window over 40 randomly selected bead∕cell bounds was 52.3 pN. Then, we assessed the responses of the cells with modulation of the cytoskeletons, namely the ubiquitous actin-microfilaments and microtubules, plus the differentiation-dependent keratin intermediate filaments. Notably, a destabilization of the first two networks led to around 50 and 30% reductions in the mean equilibrium forces, respectively, relative to untreated cells, whereas a loss of the third one yielded a 25% increase. The differences in the forces from untreated and treated cells are resolved by the optical tweezers experiment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".