Simultaneous vibration and high-speed microscopy to study mechanotransduction in living cells
Bibliographic record
Abstract
Cells exhibit the ability to sense and respond to local mechanical stimuli, leading to changes in function. This capability, referred to as mechanotransduction, is essential to normal tissue function, but the exact mechanisms by which cells sense local forces (strain, shear, compression and vibration) remain unclear. Recent studies in small animals and humans indicate that the frequency of cyclic mechanical stimuli is important, with physiological responses observed for stimuli ranging between 1 and 90 Hz. To better understand the cellular and molecular mechanisms underlying mechanotransduction, it will be important to observe cells in real time, using optical microscopy during high-frequency mechanical stimulation. We have developed a motion-control platform that can produce sinusoidal vibration of live cells during simultaneous high-speed microscopy and fluorimetry, at frequencies up to 100 Hz with peak acceleration up to 9.8 m s-2. The platform is driven by a voice coil and acceleration is measured with an accelerometer (Dytran 7521A1). The motion waveform was verified by high-speed imaging, using a digital camera (Casio EX-F1) operating at 1200 frames s-1 attached to an inverted microscope (Nikon Diaphot). When operating at 45 Hz and 2.94 m s-2 peak acceleration, the observed motion waveform exhibited sinusoidal behaviour, with measured peak-to-peak amplitude of 72 μm. Cultured osteoblast-like cells (UMR-106) were subjected to 2.94 m s-2 vibration at 45 Hz and remained attached and viable. This device provides - for the first time - the capability to mechanically stimulate living cells while simultaneously observing responses with optical microscopy.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".