Data from: Investigating the combined effects of jadomycin B and celecoxib against triple-negative breast cancer using zebrafish larval xenografts
Bibliographic record
Abstract
Breast cancer affects 1 in 8 Canadian women over their lifetime. Triple-negative breast cancer (TNBC) represents 10-20 % of all advanced stage breast cancers, often developing multi-drug resistance (MDR), commonly resulting in treatment failure. Jadomycin B (JB), a natural product of Streptomyces venezuelae, maintains cytotoxicity against MDR TNBCs, shown to be enhanced when combined with selective COX-2 inhibitor, celecoxib (CXB). Our objectives were to generate a fluorescent human TNBC cell line, as well as evaluate the toxicity and anticancer effect of JB combined with CXB using zebrafish larval xenografts. Fluorescent human TNBC MDA-MB-231 cells (231-EGFP) were generated and characterized for zebrafish larval xenografts. A maximum tolerated dose (MTD) in zebrafish larvae was determined for JB (20 mM) and CXB (5 mM). Zebrafish embryos were xenotransplanted with 231-EGFP cells and treated with the MTD of JB CXB. 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 the safety and efficacy of JB combined with CXB in a zebrafish larval xenograft model for human TNBC, enhancing anticancer effects, similar to 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".