Evolutionary Hypothesis in Cell Cycle of Breast Cancer Patients: Mosaic Phases in Single Cancer Cells
Bibliographic record
Abstract
Introduction: Cell cycle shapes the initiation, progression and therapeutic approaches of neoplasms. An uncontrolled cell proliferation and growth are the key characteristics of either malignant or benign tumors. The programmed check points control the transition of phases through the related barriers. Therefore, balancing the carcinogenic processes may inhibit progression and facilitate a targeted-base therapy. Methods: The present study is performed in interphase. Detection of the Mosaic Phases (MPs) by Fluorescence In Situ Hybridization was confirmed by assaying the protein expression (PE) including immunofluorescence and flow cytometry. Results: The novel hypothesis reflects the presence of dual and/or multi-phases, as minor clones in single cells of breast cancer (BC) patients. This finding led to initiate a model with applicable ratio values and different MPs including G1/S, S/G2 and G1/S/G2, accompanied by normal phases (G1, S, G2). The remarkable harmonic behaviors between signal copy numbers and the corresponding PE, dual- and triple- co-expression between different cyclins combination including E/B1 and D1/E/B1 and the other involved proteins were observed. The ratio of gain to normal signals appeared to be a good prognosis for chromosome 1, but better survival was significantly obtained for this ratio in chromosome 3 Conclusion: The predisposing-diagnostic-predictive-prognostic-preventive panels may lead to innovate the CDKs inhibitor-based therapy by considering the MPs Model; and may also be considered for clinical classification, in BC and other cancers.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".