Dietary Flaxseed–Trastuzumab Interactive Effects on the Growth of HER2-Overexpressing Human Breast Tumors (BT-474)
Bibliographic record
Abstract
Flaxseed (FS) reduces breast tumorigenesis and human epidermal growth factor receptor 2 (HER2) expression in postmenopausal patients and animal models. The primary treatment for HER2-overexpressing tumors is trastuzumab (TRAS). FS oil enhances TRAS effectiveness in athymic mice but the FS effect is unknown and was therefore determined. Athymic mice with established BT-474 tumors were fed the basal diet (control), or 10% FS diet, with or without TRAS (2.5mg/kg) treatment for 5 wk. After 2 wk, TRAS and FS reduced tumor size with a trend for an FS × TRAS interaction; however, after 5 wk, only TRAS reduced tumor size and increased tumor apoptosis. FS did not further improve TRAS effect but increased overall survival. TRAS reduced signaling biomarkers [phosphorylated HER2 and mitogen-activated protein kinase (MAPK) proteins; Akt1, Akt2, MAPK, and estrogen receptor-α mRNA], FS reduced phosphorylated-Akt1 protein, and FS × TRAS interactions were seen for HER2 mRNA and phosphorylated-Akt1 protein. FS, with and without TRAS, increased tumor n-3 PUFA levels and serum lignans indicating potential roles in the observed effect. In conclusion, TRAS reduces tumor growth by influencing HER2 signaling. Dietary FS has minimal tumor-reducing effect, does not interfere with TRAS action, but improves overall survival in athymic mice.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".