Biosynthetic Pathways of Ginsenosides and Polysaccharides in Panax ginseng
Bibliographic record
Abstract
Ginseng (Panax ginseng C.A. Meyer), as a traditional Chinese medicinal material and an important economic crop, its main active components, ginsenosides and polysaccharides, have demonstrated pharmacological effects in terms of antioxidation, anti-inflammation, immune regulation and neuroprotection.This study systematically expounds the progress in the biosynthesis of ginsenosides and polysaccharides, including the roles of precursor substances and metabolic pathways, key rate-limiting enzymes, cytochrome P450 and glycosyltransferases in ginsenoside synthesis, as well as the regulation of polysaccharide synthetase and monosaccharide activation pathways in polysaccharide formation.The roles of transcription factors, signal transduction pathways and epigenetics in the regulation of synthetic pathways were further explored, and the applications of transcriptomics, proteomics and metabolomics in revealing key genes and metabolic networks were summarized.Synthetic biology and metabolic engineering have provided new ideas for the efficient production of saponins and polysaccharides, but there are still problems such as insufficient functional gene identification, incomplete pathway analysis, and restricted application transformation.In-depth research on the biosynthetic pathways of ginsenosides and polysaccharides is conducive to the efficient development and utilization of ginseng resources, and also provides a theoretical basis and technical support for the metabolic improvement of medicinal plants and the modernization of traditional Chinese medicine.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".