Flaxseed Lignans as Important Dietary Polyphenols for Cancer Prevention and Treatment: Chemistry, Pharmacokinetics, and Molecular Targets
Bibliographic record
Abstract
Cancer causes considerable morbidity and mortality across the world. Socioeconomic, environmental, and lifestyle factors contribute to the increasing cancer prevalence, bespeaking a need for effective prevention and treatment strategies. Phytochemicals like plant polyphenols are generally considered to have anticancer, anti-inflammatory, antiviral, antimicrobial, and immunomodulatory effects, which explain their promotion for human health. The past several decades have contributed to a growing evidence base in the literature that demonstrate ability of polyphenols to modulate multiple targets of carcinogenesis linking models of cancer characteristics (i.e., hallmarks and nutraceutical-based targeting of cancer) via direct or indirect interaction or modulation of cellular and molecular targets. This evidence is particularly relevant for the lignans, an ubiquitous, important class of dietary polyphenols present in high levels in food sources such as flaxseed. Literature evidence on lignans suggests potential benefit in cancer prevention and treatment. This review summarizes the relevant chemical and pharmacokinetic properties of dietary polyphenols and specifically focuses on the biological targets of flaxseed lignans. The consolidation of the considerable body of data on the diverse targets of the lignans will aid continued research into their potential for use in combination with other cancer chemotherapies, utilizing flaxseed lignan-enriched natural products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".