A characterization of mitotic and centrosomal defects in a continuum model of Breast Cancer
Bibliographic record
Abstract
ABSTRACT Errors in mitosis can contribute to aneuploidy and CIN and play a pivotal role in cancer. So the identification of altered mitotic regulators can contribute to the understanding of the development and progression of breast cancer. In the present study we used an in vitro model of disease progression (the MCF10A series of BC continuum) and analyzed the errors of chromosome segregation that occur during the progression of the disease. Our findings indicated that the MCF10A series exhibited several abnormalities in chromosome segregation and its frequency increased with the disease progression. These errors included anaphase lagging chromosomes, micronuclei, nuclear buds, nucleoplasmic bridges, errors of chromosome alignment, and centrosome loss/amplification. Moreover, the presence of centrosome amplification disrupted the proper orientation of the mitotic spindle, resulting in the generation asymmetrical cell lines and aneuploidy in the MCF10A series. Hyper stable kinetochore-microtubule (kt-MT) attachment was also found in premalignant, preinvasive, and invasive cell lines, which can also explain the presence of errors of chromosome alignment. The human transcriptome array also determined possible negative regulators of ciliogenesis that can explain the mechanism of chromosome missegregation that lead to CIN found in the MCF10A series. Collectively, these findings highlight the importance of mitotic defects in the progression of breast cancer.
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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) | 0.002 | 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".