Cellular deformation characterization of human breast cancer cells under hydrodynamic forces
Why this work is in the frame
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Bibliographic record
Abstract
Understanding how cells sense mechanical forces, and how respond biologically to themis an interesting and quickly-progressing area. Cells within their microenvironment are subjected tovarious physical forces such as mechanical loads and shear stress. Cells respond and adjust to theseforces by mechanotransduction mechanism in which deformation and mechanical forces are convertedinto biomechanical signals. To quantify mechanotransduction responses and to correctly interpretthe behavior of cell under <em>in vitro</em> stimulation, magnitude and distribution of the stresses on the cellmembrane should be characterized. In this study, a 2D Finite Element Model is introduced to simulatethe deformation of individual benign (MCF10A) and malignant (MCF7) human breast cancer cellsunder hydrodynamic forces. A fluid-structure interaction method is implemented to model fluid flowand the adherent single cells inside a microchannel to study the nature of mechanical forces (viscousand pressure) and to determine their contribution to the deformation of cells. Due to the differentmechanical properties, cells respond differently to the forces exerted by the fluid flow. It was foundthat the maximum stress and strain take place at the interface of the adherent cell and channel wall. Also, under the same boundary conditions, nucleolus and cytoplasm of an individual malignant cellundergo more deformation comparing a single benign cell. Furthermore, it was observed that both two cell lines experience much more stress when their attached area to the substrate is reduced.
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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 it