Treatment Timing of Triple-Negative Breast Cancer
Bibliographic record
Abstract
Breast cancer is the second highest cause of death from cancer in Canada. Triple-negative breast cancer (TNBC) accounts for 10-15% of all cases and has a poorer prognosis than other breast cancer subtypes. TNBC lacks expression of the estrogen receptor, progesterone receptor, and the human epidermal growth factor receptor 2 (HER2), which are common therapeutic targets in breast cancer. The standard of care for treatment of TNBC instead consists of adriamycin (A), paclitaxel (T), carboplatin (Ca), and cyclophosphamide (C), to target various aspects of the cell cycle in order to induce cell cycle arrest. Timing of administration may affect cell cycle arrest, and alterations in cell cycle mediators may also influence the efficacy of treatment. The purpose of this study was to determine how the addition and timing of treatments influence the cell cycle and how this knowledge can be used to help determine more effective timing of treatment administrations. MDA-MB-231 TNBC cells were treated with AC, T, T+Ca, or Ca at various time points. Flow cytometry and Trypan Blue exclusion assay were used to determine cell cycle progression and proliferation rate. It was found that different combinations of drugs resulted in the arrest of cells at various phases of the cell cycle which may affect responsiveness to subsequent treatments. This information can be used to help determine the most effective timing of treatment and may help improve the 5-year survival rate of patients with TNBC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".