Triterpenic Acid Content and Cytotoxicity of Some <i>Salvia</i> Species From Iran
Bibliographic record
Abstract
For prosperous domestication, breeding, and cultivation of a herbal species, it is important to screen its medicinally valuable compounds as well as its referred biological activity. Salvia L. species (Lamiaceae), distributed throughout the world, contain a wide range of secondary metabolites including terpenoids and phenolic derivatives. Betulinic acid (BA), oleanolic acid (OA), and ursolic acid (UA) are highly valuable triterpenic acids (TAs) because of their wide range of biological activities. The objective of the present work was to evaluate the BA, OA, and UA contents among 22 Salvia species native to Iran. TA content in the studied Salvia species was compared with that in Salvia officinalis as a commercial species. High-performance liquid chromatography with photodiode array detector results showed that the maximum content of BA (3.12 ± 0.03 mg/g dry weight [DW]) and OA (1.96 ± 0.05 mg/g DW) was determined in Salvia multicaulis. The highest content of UA (4.34 ± 0.1 mg/g DW) was quantified in S. officinalis L. followed by S. multicaulis (3.71 ± 0.08 mg/g DW). Salvia multicaulis exhibited significantly higher agro-morphological values than S. officinalis in traits related to plant width, leaf length, internode length, and inflorescence length. The cytotoxicities of both species were determined against human cancer cell lines using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The methanolic extract of S. multicaulis and S. officinalis showed cytotoxic effects against SH-SY5Y and MCF-7 cell lines, respectively. Both species were equally cytotoxic against the HL-60 cell line. This study provides scope for the selection of high-yielding species and genetic improvement through breeding and biotechnological programs in the future.
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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 | 0.000 |
| 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) | 0.000 | 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".