Yeni Çalışmalar Işığında Hypericum Türlerinin Farmakolojik Aktiviteleri
Bibliographic record
Abstract
The Hypericum species that belong to the family Hypericaceae, especially the most common Hypericum perforatum, are presently one of the most consumed medicinal plants in the world. In recent years, the use of products containing H. perforatum has increased dramatically all over the world.1 There is evidence that H. perforatum has been used for its wound healing and antidepressant effects since ancient times and was even believed to be “sacred” and was a part of religious rituals in medieval Europe.2 H. perforatum is known as “sarı kantaron” in Turkish, whereas the most common name in other countries is “St. John’s Wort”. Numerous studies have been conducted on the chemical constituents and biological activities of the Hypericum species. The major active constituents are considered to be hyperforin which is a phloroglucinol derivative, and hypericin, a naphthodianthrone compound. Furthermore, other biologically active constituents, such as flavonoids, phenolic acids, tannins, volatile compounds and xanthones are also present in the plant extracts.3 Several pharmacological activities of the Hypericum species have been documented so far. The present study gives a summary of the most recent studies related to some important properties, including its antidepressant effect, wound-healing effect, anti-inflammatory effect, antioxidant effect, antimicrobial effect, effects on neurodegenerative disorders, cytotoxic effect, analgesic, and antinociceptive effect.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.012 |
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".