A biomechanical model for the transendothelial migration of cancer cells
Bibliographic record
Abstract
We propose a biomechanical model for the extravasation of a tumor cell (TC) through the endothelium of a blood vessel. Based on prior in vitro observations, we assume that the TC extends a protrusion between adjacent endothelial cells (ECs) that adheres to the basement membrane via focal adhesions (FAs). As the protrusion grows in size and branches out, the actomyosin contraction along the stress fibers (SFs) inside the protrusion pulls the relatively rigid nucleus through the endothelial opening. We model the chemo-mechanics of the SFs and the FAs by following the kinetics of the active myosin motors and high-affinity integrins, subject to mechanical feedback. This is incorporated into a finite-element simulation of the extravasation process, with the contractile force pulling the nucleus of the TC against elastic resistance of the ECs. To account for the interaction between the TC nucleus and the endothelium, we consider two scenarios: solid-solid contact and lubrication by cytosol. The former gives a lower bound for the required contractile force to realize transmigration, while the latter provides a more realistic representation of the process. Using physiologically reasonable parameters, our model shows that the SF and FA ensemble can produce a contractile force on the order of 70 nN, which is sufficient to deform the ECs and enable transmigration. Furthermore, we use an atomic force microscope to measure the resistant force on a human bladder cancer cell that is pushed through an endothelium cultured in vitro. The magnitude of the required force turns out to be in the range of 70-100 nN, comparable to the model predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".