Advances in Biosynthetic Pathways of Phenylpropanoids in <i>Angelica sinensis</i>
Bibliographic record
Abstract
As one of the most representative medicinal plants of traditional Chinese medicine, Angelica sinensis contains a variety of bioactive constituents, in which phenylpropanoids serve as major functional compounds and are reported to possess significant pharmacological activities, involving antioxidant, anti-inflammatory, immunomodulatory, circulatory-enhancing, and neuroprotective effects.In the context of advances in molecular biology, metabolomics, and synthetic biology, great progress has been made in recent years regarding the investigation into the biosynthetic pathways of phenylpropanoids in A. sinensis.This study firstly describes the structural characteristics and classification of phenylpropanoids, outlines their distribution pattern and functional relevance in A. sinensis, and highlights recent progress regarding the identification of key biosynthetic enzymes, including PAL, C4H, 4CL, COMT, and CCoAOMT, regulatory factors (including transcription factors such as MYB, bHLH, and WRKY), and associated signaling mechanisms.Furthermore, it summarizes the application of multi-omics integration, gene editing, metabolic engineering, and synthetic biology platforms in unlocking biosynthetic mechanisms and enhancing the production of target compounds.In addition, this review considers the influence of environmental conditions, developmental stages, and hormonal signaling on phenylpropanoid biosynthesis.This study provides fundamental theoretical insights into the comprehensive biosynthetic network of phenylpropanoids in A. sinensis and lays a foundation for their innovative development.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".