Investigating longitudinal changes in the mechanical properties of MCF-7 cells exposed to paclitaxol using particle tracking microrheology
Bibliographic record
Abstract
Evidence suggests that compression and shear wave elastography are sensitive to the mechanical property changes occuring in dying cells following chemotherapy, and can hence be used to monitor cancer treatment response. A qualitative and quantitative understanding of the mechanical changes at the cellular level would allow to better infer how these changes affect macroscopic tissue mechanical properties and therefore allow the optimization of elastographic techniques (such as shear wave elastography) for the monitoring of cancer therapy. We used intracellular particle tracking microrheology (PTM) to investigate the mechanical property changes of cells exposed to paclitaxol, a mitotic inhibitor used in cancer chemotherapy. The average elastic and viscous moduli of the cytoplasm of treated MCF-7 breast cancer cells were calculated for frequency ranges between 0.2 and 100 rad s(-1) (corresponding to 0.03 and 15.92 Hz, respectively). A significant increase in the complex shear modulus of the cell cytoplasm was detected at 12 h post treatment. At 24 h after drug exposure, the elastic and viscous moduli increased by a total of 191.3 Pa (>8000×) and 9 Pa (∼9×), respectively for low frequency shear modulus measurements (at 1 rad s(-1)). At higher frequencies (10 rad s(-1)), the elastic and viscous moduli increased by 188.5 Pa (∼60×) and 1.7 Pa (∼1.1×), respectively. Our work demonstrates that PTM can be used to measure changes in the mechanical properties of treated cells and that cell elasticity significantly increases by 24 h after chemotherapy exposure.
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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 | 0.000 | 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".