THE STUDY OF THE FLAVONOIDS AND ANTIDEPRESSANT ACTIVITY OF THE LEAVES AND LIQUID EX-TRACT OF CRATAEGUS SUBMOLLIS
Bibliographic record
Abstract
Nowadays flowers and fruits of various types of hawthorn are widely used as cardiotonic drugs in Russian Federation. In our opinion a promising species for harvesting raw materials is Quebec hawthorn (Crataegus submollis Sarg., Rosaceae). Quebec hawthorn is successfully cultivated on the territory of the Russian Federation and is characterized by rapid growth and high yield in comparison with wild species. An additional point is that one of the possible types of raw materials are hawthorn leaves widely used abroad. Quebec hawthorn leaves contain flavonoids, among which there is a hyperoside. The maximum of the absorption curve of the extract solution of Quebec hawthorn leaves with an aluminum chloride solution of 412 nm. We have developed a quantification method for determination the total flavonoids in the Quebec hawthorn leaves calculated as hyperoside. It was established that the optimum extraction solvent for this raw material is 70% ethanol. The level of content and dynamics of accumulation of the total flavonoids in the Quebec hawthorn leaves during the growing season of 2017 was studied. The maximum content of total flavonoids in the Quebec hawthorn leaves (3.12 ± 0.05% calculated on hyperoside) is observed in May, during plant flowering. The liquid extract based on Quebec hawthorn leaves possesses moderate antidepressant activity. For this reason, leaves of Quebec hawthorn leaves are promising medicinal plant raw materials.
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 | 0.000 | 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".