Potential Health Benefits of Plant Food-Derived Bioactive Components: An Overview
Bibliographic record
Abstract
Plant foods are consumed worldwide due to their immense energy density and nutritive value. Their consumption has been following an increasing trend due to several metabolic disorders linked to non-vegetarian diets. In addition to their nutritive value, plant foods contain several bioactive constituents that have been shown to possess health-promoting properties. Plant-derived bioactive compounds, such as biologically active proteins, polyphenols, phytosterols, biogenic amines, carotenoids, etc., have been reported to be beneficial for human health, for instance in cases of cancer, cardiovascular diseases, and diabetes, as well as for people with gut, immune function, and neurodegenerative disorders. Previous studies have reported that bioactive components possess antioxidative, anti-inflammatory, and immunomodulatory properties, in addition to improving intestinal barrier functioning etc., which contribute to their ability to mitigate the pathological impact of various human diseases. This review describes the bioactive components derived from fruit, vegetables, cereals, and other plant sources with health promoting attributes, and the mechanisms responsible for the bioactive properties of some of these plant components. This review mainly compiles the potential of food derived bioactive compounds, providing information for researchers that may be valuable for devising future strategies such as choosing promising bioactive ingredients to make functional foods for various non-communicable disorders.
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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 | 0.003 | 0.002 |
| 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) | 0.003 | 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".