Live Imaging to Study Microtubule Dynamic Instability in Taxane-resistant Breast Cancers
Bibliographic record
Abstract
Taxanes such as docetaxel belong to a group of microtubule-targeting agents (MTAs) that are commonly relied upon to treat cancer. However, taxane resistance in cancerous cells drastically reduces the effectiveness of the drugs' long-term usage. Accumulated evidence suggests that the mechanisms underlying taxane resistance include both general mechanisms, such as the development of multidrug resistance due to the overexpression of drug-efflux proteins, and taxane-specific mechanisms, such as those that involve microtubule dynamics. Because taxanes target cell microtubules, measuring microtubule dynamic instability is an important step in determining the mechanisms of taxane resistance and provides insight into how to overcome this resistance. In the experiment, an in vivo method was used to measure microtubule dynamic instability. GFP-tagged α-tubulin was expressed and incorporated into microtubules in MCF-7 cells, allowing for the recording of the microtubule dynamics by time lapse using a sensitive camera. The results showed that, as opposed to the non-resistant parental MCF-7CC cells, the microtubule dynamics of docetaxel-resistant MCF-7TXT cells are insensitive to docetaxel treatment, which causes the resistance to docetaxel-induced mitotic arrest and apoptosis. This paper will outline this in vivo method of measuring microtubule dynamic instability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".