Identification and quantitative determination of the flavonoids of the complex dense extract of st. john's wort herb and pot marigold flowers
Bibliographic record
Abstract
Common Saint-John's wort (Hypericum perforatum) and pot marigold (Calendula officinalis) are rich in such biologically active substances (BAS) as carotene, ascorbic acid, essential oils, vitamins, tannin and resinous substances, as well as flavonoids that bear evident wound healing properties and antiulcerous properties. The object of this study was BAR composition of the complex dense herb extract of St. John's wort and flowers of marigolds (1:10). In order to introduce a new herbal substance into medical practice, it is necessary to develop methods for its identification and quantification. The TLC [thin layer chromatography] method was used to identify the BAR in the extract under study, and the method of absorption spectrophotometry was proposed for quantification of the content of flavonoids. As a result of the conducted research, there were selected characteristic substances - identification markers of the extract, the choice of which was in accordance with the requirements of the SPF on the quality of the herb of St. John's wort and the flowers of pot marigold, and there was indicated the position and coloring of the zones in the chromatographic profile of the tested extract solution. Such approach will enable objective identification of the extract as a substance and as an active pharmaceutical ingredient in the formulation. The criterion for quantitative standardization of the complex dense extract is the content of the amount of flavonoids not less than 1.5% in terms of hyperoside and dry substance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".