Biotechnology of flavonoids and other phenylpropanoid‐derived natural products. Part I: Chemical diversity, impacts on plant biology and human health
Bibliographic record
Abstract
Plant natural products derived from phenylalanine and the phenylpropanoid pathway are impressive in their chemical diversity and are the result of plant evolution, which has selected for the acquisition of large repertoires of pigments, structural and defensive compounds, all derived from a phenylpropanoid backbone via the plant-specific phenylpropanoid pathway. These compounds are important in plant growth, development and responses to environmental stresses and thus can have large impacts on agricultural productivity. While plant-based medicines containing phenylpropanoid-derived active components have long been used by humans, the benefits of specific flavonoids and other phenylpropanoid-derived compounds to human health and their potential for long-term health benefits have been only recognized more recently. In this part of the review, we discuss the diversity and biosynthetic origins of phenylpropanoids and particularly of the flavonoid and stilbenoid natural products. We then review data pertaining to the modes of action and biological properties of these compounds, referring on their effects on human health and physiology and their roles as plant defense and antimicrobial compounds. This review continues in Part II discussing the use of biotechnological tools targeting the rational reconstruction of multienzyme pathways in order to modify the production of such compounds in plants and model microbial systems for the benefit of agriculture and forestry.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".