Mechanical changes in the epithelial to mesenchymal transition
Bibliographic record
Abstract
Cell motility is essential in many physiological events such as embryonic morphogenesis and tissue repair.Abnormal cell migration can lead to various pathological events including cancer metastasis.Therefore, understanding physical changes associated with cell migration is important to our understanding of many biological processes.One specific biological transformation with far ranging cell motility impact in physiology and pathology is the Epithelial to Mesenchymal Transition (EMT); in this process, epithelial cells lose cell-cell contacts and become more migratory and invasive mesenchymal cells.While important for diverse biological processes, this transition is believed to be a key phenomenon in cancer metastasis.Therefore, studying physical changes that cells undergo during EMT is critical for understanding the metastatic hallmark of increased cancer cell migration.In this thesis, I studied the changes in contractile forces and work during EMT utilizing a new soft silicone-based Traction Force Microscopy (TFM) assay that I developed to study contractile forces that adherent crawling cells apply on their environment.Using our new polydimethylsiloxane (PDMS) assay, I found there was a significant increase in contractile work and stress.Furthermore, I observed that cells change actin architecture and increase their shear moduli as they transition from epithelial phenotype to mesenchymal phenotype.To better understand how migratory behavior of the cells changes during this transition, I examined motility of individual cells in a monolayer sheet by tracking nuclei of cells, revealing a transition from diffusive to ballistic movement during EMT.Together, these studies help us to have a more comprehensive knowledge of biophysics of EMT and cancer metastasis.This may open the door to new biophysical approaches to diagnosis and therapy for the disease.Beyond EMT, I also
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".