Fructus Ligustri Lucidi modulates estrogen receptor expression with no uterotrophic effect in ovariectomized rats
Bibliographic record
Abstract
Accumulating evidence suggests that Fructus Ligustri Lucidi (FLL) plays a beneficial role in preventing the development of osteoporosis. However, the effects of FLL on estrogen receptor (ER) α and ERβ expressions remain unknown. Therefore, in the current study we attempted to probe into the effects of FLL on ERα and ERβ expressions in femurs, tibias and uteri of ovariectomized (OVX) rats. The OVX rats were orally administrated with FLL water extract (3.5 g/kg/day) for 12 weeks. The uteri, femurs, tibias and serum were harvested from rats. The serum levels of estrogen (E 2 ), luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were determined by ELISA. The expressions of ERα and ERβ in the femurs and tibias as well as uteri were analysed by western blot and immunohistochemical staining. FLL treatment did not increase uterus relative weight in OVX rats. Further, FLL treatment increased ERα expression in the femurs and tibias, and enhanced ERβ expression in the uteri of OVX rats. However, the resulted expression of ERα was stronger than that of ERβ in OVX rats in response to FLL treatment. Meanwhile, administration with FLL to OVX rats increased FSH and LH but did not increase E 2 level in the serum. FLL treatment shows tissue selection on ERα and ERβ expressions in the femurs and tibias as well as uteri of OVX rats without uterotrophic effect, which may offer the scientific evidence of the efficiency and safety of its clinical application.
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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 | 0.001 |
| 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".