Effect of St. John's Wort (<i>Hypericum perforatum</i>) on obesity, lipid metabolism and uterine epithelial proliferation in ovariectomized rats
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: This study was conducted to assess the potential of St. John's Wort (Hypericum perforatum) to prevent obesity and abnormalities in lipid metabolism induced by ovariectomy in a rat model without stimulatory activity on uterus. MATERIALS/METHODS: Ovariectomized (OVX) rats were treated for 6 weeks with 70% ethanol extracts of Hypericum perforatum [HPEs: whole plant (WHPE) and flower and leaves (FLHPE)], β-estradiol-3-benzoate at a dose of 50 µg/kg/day (E2) or vehicle (distilled water). RESULTS: As expected, OVX increased body weight gain and adiposity and showed higher food efficacy ratio. OVX also increased the serum cholesterol as well as insulin resistance, while reducing uterus weight and uterine epithelial proliferation rate. HPEs (WHPE and FLHPE) showed estrogen-like effect on body weight gain, adipose tissue weight and food efficacy ratio in OVX rats. HPEs prevented hypercholesterolemia induced by OVX more effectively than E2. E2 increased uterus weight and epithelial proliferation rate in OVX rats, while HPEs maintained them at the level of the sham-operated animals. CONCLUSIONS: Our finding demonstrates that HPEs can be considered as an effective agent to prevent OVX-induced obesity without stimulatory activity on uterus.
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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 | 0.000 | 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 | 0.001 |
| 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".