Role of St. John'S Wort (Hypericum Perforatum L.) in the Management of Diabetes and Neurological Disorders
Bibliographic record
Abstract
2St. John’s Wort (Hypericum perforatum L.) is a widely utilized antidepressant plant, which has been used as both therapeutic drug and over-the-counter (OTC) product in all around the world. There are many ethnomedical, experimental, and clinical studies demonstrating its antidepressant activities. It seems to be quite effective on the treatment of mild-to-moderate depression, as reported in various controlled clinical trials. In addition to its well-known antidepressant activities, St. John’s Wort has been reported to be used in folkloric medicine for the cure of diabetes mellitus. We have previously reported that extracts of aerial parts of H. perforatum normalize blood glucose and pain perception and depression/anxiety levels in streptozotocin (STZ) diabetic rats. On the other hand, clinical management of depression is a dilemma in diabetic patients. Most of antidepressant agents may interfere with glycemic controls by increasing or decreasing blood glucose levels in depressive patients with diabetes mellitus. On the other side of this clinical problem, there are drug–drug interactions interfering both antidepressant and antidiabetic treatments. Hence, preparations of St. John’s Wort seem to be most appropriate cure for the diabetic patients having mild-to-moderate depression.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".