Assaying the variation in secondary metabolites of St. John's wort for its better use as an antibiotic
Bibliographic record
Abstract
The present study is aimed at investigating the effects of variation in secondary metabolites of St. John’s wort for its better use as an antibiotic. The seasonal dynamics investigation of St. John’s wort secondary metabolites was carried out on annual, biennial and triennial wild-growing plants of the suburban localities, and on the indigenous perennial plants of the mountainous localities. The effects of variation in secondary metabolites of the plant material were monitored using a complex antimicrobial preparation imanin. As plant secondary metabolites, imanin was isolated from flowers and leaves of St. John’s wort by aqueous-alkaline extraction. The quality of imanin contained in St. John’s wort was determined by FTIR and HPLC methods. The imanin extracts were tested for antimicrobial activity against Staphylococcus aureus, Streptococcus agalactiae,Bacillus diphteriae, Bacillus tetani, Clostridium histolyticus, Bacillusmesentericus and Bacillus mycoides. The quantitative effects of temperature and light intensity on imanin accumulations in St. John’s wort were examined depending on the sampling periods and location. The results of antimicrobial activity and quantitative effects of climatic conditions were correlated with vegetation phases of hypericum plants. Key words: Imanin, St. John’s wort, antimicrobial activity, hyperforin, plant vegetation.
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".