Phytochemical study of phenolic compounds of labrador tea (Lédum palústre L.)
Bibliographic record
Abstract
Aim. This work is devoted a phytochemical study of biologically active substances in herb of Labrador Tea (Lédum palústre L.), as well as investigation of the possibility of complex use of raw materials in order to create on its basis new herbal medicines.Methods. The object of the study served as the herb Labrador Tea. Separation of isolated substances was performed by adsorption and partition chromatography on various adsorbents. The structure of the isolated compounds was determined based on physicochemical methods: paper (PC) and the thin layer (TLC) chromatography, UV, IR and NMR spectroscopy in comparison with the original valid standards of flavonoids. Acid hydrolys was used for the determination of the flavonoid aglycone composition. The content of total flavonoids was determined by differential spectrophotometry at a wavelength of 412 nm from the reaction with aluminum chloride based on the hyperoside-standard (Ukrainian scientific Pharmacopoeial center for quality of medicines (Pharmacopoeial center), Kharkоv).Results. During this study we found more than 40 of phenolic compounds and identified 31compounds. The phytochemical analysis showed the presence of flavonols (11 compounds), catechins (5). Hydroxycinnamic acids represented by caffeic, ferulic, chlorogenic acids. Besides that, it were found phenolic glycoside arbutin, coumarins - coumarin, umbelliferon, scopoletin, esculetin and esculin, tannins – metyl gallate, pyrogallol.Conclusions. The following biologically active substances were defined for the first time: flavonoids – 5-methyl-kaempferol, avicularin, polistahozid, quercitrin; coumarins – esculetin, esculin; as well as hydroxycinnamic acids – ferulic, chlorogenic, neochlorogenic of Labrador Tea. The obtained data justify the prospectivefor creation of new and effective herbal medicines from Labrador Tea
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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) | 0.002 | 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".