Bibliographic record
Abstract
Chemotherapy resistance is a significant challenge in colorectal cancer (CRC) therapy. Recent studies have identified drug-tolerant persister cells (DTPs) as key contributors to non-genetic resistance. DTPs are a subpopulation of cancer cells that are slow-cycling or non-proliferative, evading treatment and resuming growth once treatment pressure is lifted. This project develops additional DTP models, including (1) a syngeneic and (2) a breast cancer model. In vivo trials with murine CRC cell lines (CT26, MC38) yielded promising results. While irinotecan (CPT-11) alone did not induce tumour regression, combination therapies with 5FU/LV, oxaliplatin (OX), and cyclophosphamide (CP) were effective but toxic —the addition of anti-PD1 produced variable regrowth. Most treated CT26 tumours were no longer palpably detectable, while all MC38 tumours maintained this state, with no regrowth observed. The final regimen of 5-FU/LV, OX, and CPT-11 stabilized growth but did not achieve regression, with growth was observed roughly three weeks on treatment. In vitro studies revealed that colorectal cancer (CT26, MC38) and breast cancer (MDA-MB-231, BT474) cell lines survive treatment by entering a drug-tolerant state, characterized by key DTP hallmarks such as regrowth, maintained sensitivity to re-treatment, slow cycling, and hypo-transcription.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".