Determination of Flavonoid and Polyphenol Compounds in Viscum Album and Allium Sativum Extracts
Bibliographic record
Abstract
Ethnopharmacology is a new interdisciplinary science that appeared in Europe of the 90, in France, as a necessity claimed by the return to the traditional remedies of each nation. The aim of this study is to identify and quantify the active ingredients of the species Viscum album and Allium sativum, in order to provide a complex chemical characterisation, which is necessary for the use of these plants extracts as natural ingredients in the pharmaceutical industry. The following methods were used: (1) the plant material was harvested from the west-side of Romania (Europe) in July 2014; (2) it was dried quickly and the main active principles were extracted using ethylic alcohol solution (50%); (3) the quantitative analyses of the flavonoids and polyphenols were performed according to a procedure described in the Romanian Pharmacopoeia. FT-IR results showed that the Viscum album extract had the highest content of polyphenolic compounds, for both flavonoids and polyphenols. This is the reason why it can be concluded that alcoholic extracts of mistletoe must be used as supplements for diabetics who require diets with flavonoids or for patients with cancers, degenerative diseases, and particularly cardiovascular diseases, who need an increased amount of polyphenols.DOI: http://dx.doi.org/10.3329/icpj.v4i5.22861 International Current Pharmaceutical Journal, April 2015, 4(5): 382-385
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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 | 0.002 | 0.001 |
| 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".