Molecular Mechanisms and Pathways as Targets for Cancer Prevention and Progression with Dietary Compounds
Bibliographic record
Abstract
A unique feature of bioactive food ingredients is their broad antioxidant function. Antioxidants having a wide spectrum of chemical structure and activity beyond basic nutrition; display different health benefits by the prevention and progression of chronic diseases. Functional food components are capable of enhancing the natural antioxidant defense system by scavenging reactive oxygen and nitrogen species, protecting and repairing DNA damage, as well as modulating the signal transduction pathways and gene expression. Major pathways affected by bioactive food ingredients include the pro-inflammatory pathways regulated by nuclear factor kappa B (NF-κB), as well as those associated with cytokines and chemokines. The present review summarizes the importance of plant bioactives and their roles in the regulation of inflammatory pathways. Bioactives influence several physiological processes such as gene expression, cell cycle regulation, cell proliferation, cell migration, etc., resulting in cancer prevention. Cancer initiation is associated with changes in metabolic pathways such as glucose metabolism, and the effect of bioactives in normalizing this process has been provided. Initiation and progression of inflammatory bowel diseases (IBD) which increase the chances of developing of colorectal cancers can be downregulated by plant bioactives. Several aspects of the potential roles of microRNAs and epigenetic modifications in the development of cancers have also been presented.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".