A Novel Stretching Platform for Applications in Cell and Tissue Mechanobiology
Bibliographic record
Abstract
Tools that allow the application of mechanical forces to cells and tissues or that can quantify the mechanical properties of biological tissues have contributed dramatically to the understanding of basic mechanobiology. These techniques have been extensively used to demonstrate how the onset and progression of various diseases are heavily influenced by mechanical cues. This article presents a multi-functional biaxial stretching (BAXS) platform that can either mechanically stimulate single cells or quantify the mechanical stiffness of tissues. The BAXS platform consists of four voice coil motors that can be controlled independently. Single cells can be cultured on a flexible substrate that can be attached to the motors allowing one to expose the cells to complex, dynamic, and spatially varying strain fields. Conversely, by incorporating a force load cell, one can also quantify the mechanical properties of primary tissues as they are exposed to deformation cycles. In both cases, a proper set of clamps must be designed and mounted to the BAXS platform motors in order to firmly hold the flexible substrate or the tissue of interest. The BAXS platform can be mounted on an inverted microscope to perform simultaneous transmitted light and/or fluorescence imaging to examine the structural or biochemical response of the sample during stretching experiments. This article provides experimental details of the design and usage of the BAXS platform and presents results for single cell and whole tissue studies. The BAXS platform was used to measure the deformation of nuclei in single mouse myoblast cells in response to substrate strain and to measure the stiffness of isolated mouse aortas. The BAXS platform is a versatile tool that can be combined with various optical microscopies in order to provide novel mechanobiological insights at the sub-cellular, cellular and whole tissue levels.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".