Agronomic Practices and Secondary Metabolite Accumulation in <i>Salvia miltiorrhiza</i>
Bibliographic record
Abstract
Salvia miltiorrhiza, known as Danshen, is an important medicinal herb, whose main secondary metabolites including tanshinones and salvianolic acids are considered responsible for antioxidant activity, cardiovascular protection, among other pharmacological effects.The agronomic practices of light exposure, temperature, water management, soil nutrient fertilization, planting density, pruning, cultivation patterns, and plant hormone regulation can affect the accumulation of such secondary metabolites.Using multi-omics techniques such as transcriptomics, proteomics, and metabolomics, regulatory networks related to secondary metabolite biosynthesis have been unveiled, as well as signaling pathways.Those studies provided a theoretical basis for optimization in agronomic practices.Taking into consideration different environmental factors and control methods of cultivation management that would affect the accumulation of secondary metabolites of Danshen, this study systematically summarizes the results, explains the key regulatory factors and potential synergistic interactions, and then discusses strategies for integrating the best agronomic practices with corresponding molecular studies to promote high-yield, high-quality, sustainable production.Through a synthesis of the current research achievements, the study lays a theoretical foundation for standard cultivation and industrial utilization of the active ingredients of Danshen.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".