Cellular deformation characterization of human breast cancer cells under hydrodynamic forces
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 in vitro 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.
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.
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".