Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) refers to an invasive subset of breast cancer that lacks oestrogen receptors (ER), progesterone receptors (PR) and lacks amplification of HER2 [1].Thus, these patients cannot be treated with a targeted therapy and have poorer outcomes compared to patients with other subforms of breast cancer.p53 it is the most frequently mutated gene in human cancer.Approximately 80% of patients with TNBC carry a p53 mutation.Recently, arsenic trioxide (ATO) was found to reactivate mutant p53 and convert it back to its normal wild-type form [2]. The aim of this research was to test if ATO might be a new treatment for TNBC.The ability of ATO to inhibit cell proliferation was determined using MTT assays while induction of apoptosis was measured using flow cytometry.IC 50 values for growth inhibition across 10 breast cancer cell lines ranged from 0.297-2.99μM.Inhibition of proliferation was found to be independent of the cell line molecular subtype.No significant differences were found between IC 50 values for TN vs non-TN cell lines (p=0.597) or between contact vs structural p53 mutants (p=0.481).For all cell lines investigated, ATO induced significant levels of apoptosis at a concentration of 5 μM.Although our data are preliminary, we conclude that ATO is a potential new therapy for the treatment of p53 mutated cancer, including triple negative breast cancer.Since ATO is already approved for the treatment of acute promyelocytic leukaemia (APL), it should be straightforward to repurpose it for TNBC.I would like to express my gratitude to my supervisor Professor Joe Duffy and all in the breast cancer research group in St. Vincent's University Hospital.Thank you for all your encouragement and for giving me the opportunity to work with and learn from you.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 it