Fluid–Structure Interactions Analysis of Shear‐Induced Modulation of a Mesenchymal Stem Cell: An Image‐Based Study
Bibliographic record
Abstract
Although effects of biochemical modulation of stem cells have been widely investigated, only recent advances have been made in the identification of mechanical conditioning on cell signaling pathways. Experimental investigations quantifying the micromechanical environment of mesenchymal stem cells (MSCs) are challenging while computational approaches can predict their behavior due to in vitro stimulations. This study introduces a 3D cell-specific finite element model simulating large deformations of MSCs. Here emphasizing cell mechanical modulation which represents the most challenging multiphysics phenomena in sub-cellular level, we focused on an approach attempting to elicit unique responses of a cell under fluid flow. Fluorescent staining of MSCs was performed in order to visualize the MSC morphology and develop a geometrically accurate model of it based on a confocal 3D image. We developed a 3D model of a cell fixed in a microchannel under fluid flow and then solved the numerical model by fluid-structure interactions method. By imposing flow characteristics representative of vigorous in vitro conditions, the model predicts that the employed external flow induces significant localized effective stress in the nucleo-cytoplasmic interface and average cell deformation of about 40%. Moreover, it can be concluded that a lower strain level is made in the cell by the oscillatory flow as compared with steady flow, while same ranges of effective stress are recorded inside the cell in both conditions. The deeper understanding provided by this study is beneficial for better design of single cell in vitro studies.
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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 | 0.001 | 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".