The Effect of Sesame Lignan and Flaxseed Lignan and Oil on the Growth of Human Estrogen Receptor‐Positive Breast Tumors (MCF‐7)
Bibliographic record
Abstract
Flaxseed (FS) reduces breast cancer growth perhaps due to its high lignan (secoisolariciresinol diglycoside (SDG)) and α‐linolenic acid (ALA)‐rich oil (FSO) content. Sesame seed (SS), which is also rich in lignans like sesamin (SES) but low in ALA, is tumorigenic. Like SDG, SES is converted to estrogen‐like enterodiol (ED) and enterolactone (EL). Our objective is to determine the effects of SES, SDG and FSO on breast tumor growth to explain the opposing effects of FS and SS. Estrogen‐positive breast tumor cells (MCF‐7) were tested for proliferation in vitro after 4‐day treatment with ALA (50uM), SES (1uM), ED and EL (1uM). Only ALA reduced proliferation (33%, P<0.001). Ovariectomized mice with MCF‐7 tumors were fed for 8 weeks the basal diet (BD; control) or BD supplemented with 0.1% SDG, 0.1% SES, or 4.0% FSO, at levels found in 10% FS or SS diets. Compared to control, SES and FSO reduced palpable tumor area by 23% (P<0.05) and 33% (P<0.01), respectively. FSO reduced tumor volume and weight (both 37%, P<0.05), while SES caused decreases of 21% and 25%, respectively, though not different from control or FSO. SDG had no effect. SES and FSO increased apoptosis by 91% and 110% (P<0.01), respectively, and decreased proliferation by 38% (P<0.01). SDG did not affect apoptosis but reduced proliferation by 37% (P<0.05). Thus, ALA may account for the protectiveness of FS and SES may not account for the tumorigenicity of SS. (Funded by NSERC)
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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) | 0.001 | 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".