Chemical composition and bioactive properties of <i>Sanguisorba minor</i> Scop. under Mediterranean growing conditions
Bibliographic record
Abstract
Sanguisorba minor Scop. is a perennial plant native to the Mediterranean region which exhibits several medicinal activities. In the present study, plant growth, chemical composition, and antitumor and antimicrobial properties of S. minor plants were evaluated under different growing conditions. In particular, plants were grown on different substrates, namely (A) peat, (B) peat : perlite (1 : 1) and (C) peat : perlite (2 : 1). The dry weight of the aerial parts and roots was higher for the peat treatment (A), whereas plant growth was severely affected in the peat : perlite (1 : 1) treatment (B). The major detected sugars were fructose and glucose, while the peat : perlite (2 : 1) treatment resulted in a significant increase of α-tocopherol, glucose and total sugars content, especially in the aerial parts. Oxalic acid and citric acid were the major organic acids detected, with a varied effect of growth substrate on organic acid profile. Growth substrate and plant part also had a significant effect on fatty acid composition, especially on linoleic acid and α-linolenic acid content which was higher in the peat : perlite (1 : 1) treatment in both aerial parts and roots. An impact on the composition of phenolic compounds was also observed, where the plants grown on peat : perlite (2 : 1) and peat : perlite (1 : 1) substrates had the highest content of individual and total phenolic compounds in their aerial parts and roots, respectively. Moreover, roots contained significantly higher amounts of phenolic compounds than the aerial parts. Moreover, plant extracts exhibited antiproliferative activity against four tumor cell lines (HeLa, HepG2, MCF-7 and NCI-H460) and a primary culture of porcine liver cells (PLP2), as well as significant antimicrobial properties. In conclusion, S. minor presented significant bioactive properties, while growth substrates affected the nutritional value, chemical composition, antitumor and antimicrobial properties of the species which could be probably attributed to the higher phenolic compound content and different compositions of phenolic compounds. Therefore, although the species is commonly found in the Mediterranean region, it is underexploited yet and its plant tissues could be a potential source of natural bioactive compounds with further use in pharmaceutical and medicinal applications.
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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 | 0.000 |
| 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 | 0.001 |
| 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".