Investigating the combined effects of jadomycin B and celecoxib against triple-negative breast cancer using zebrafish larval xenografts
Bibliographic record
Abstract
Breast cancer affects one in eight Canadian women over their lifetime. Triple-negative breast cancer (TNBC) represents 10%–20% of all advanced stage breast cancers, often developing multidrug resistance (MDR), commonly resulting in treatment failure. Jadomycin B (JB), a natural product of Streptomyces venezuelae, maintains cytotoxicity against MDR TNBCs, and its activity is enhanced when combined with selective cyclooxygenase-2 inhibitor, celecoxib (CXB) in vitro. Our objectives were to evaluate the toxicity and anticancer effects of JB combined with CXB using zebrafish larval xenografts as a model system. Fluorescent human TNBC MDA-MB-231 cells (231-enhanced green fluorescent protein (EGFP)) were generated and characterized for zebrafish larval xenografts. A maximum tolerated dose (MTD) in zebrafish larvae were determined for JB (20 µM) and CXB (5 µM). Zebrafish embryos were xenotransplanted with 50–100 231-EGFP cells and treated with the MTDs of JB and CXB alone or in combination. The combination of JB and CXB resulted in a 75% reduction in 231-EGFP fluorescence intensity, significantly higher than reductions caused by either drug alone (39% for JB, 15% for CXB) ( p < 0.05). This study demonstrates that combining JB with CXB enhances anticancer activity in a zebrafish larval xenograft model of human TNBC, validating effects previously determined in vitro.
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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 | 0.000 | 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) | 0.001 | 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".