Effects of Hormone Replacement Therapy on Plasma and Tissue Fibrinolytic Activity in a Rat Model of Surgically Induced Menopause
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to analyze the effects of estrogen deficiency and hormone replacement therapy (HRT) on fibrinolytic activity in a rat mode of surgically-induced menopause. METHODS: Twelve-week-old, sexually mature female Sprague-Dawley rats, each weighing 200-250 g, were randomly divided into four groups: (1) sham-operated group, (2) ovariectomy group, (3) ovariectomy group followed by oral administration of daily 17β-estradiol (0.02 mg/kg/day) (E2) + norethisterone acetate (0.01 mg/kg/day), and (4) ovariectomy group followed by oral administration of daily 17β-estradiol (0.01 mg/kg/day) + drospirenone (0.02 mg/kg/day). Tissue plasminogen activator (tPA) antigen, plasminogen activator inhibitor-1 (PAI-1) antigen, and PAI-1/tPA levels were measured as markers of fibrinolysis in plasma and liver and brain tissue. RESULTS: Compared with sham-operated rats, ovariectomized rats showed higher levels of fibrinolytic activity; however, the increased fibrinolytic activity in plasma and liver tissue was significantly reduced by HRT regimens. No change was observed in the levels of fibrinolytic activity in brain tissue. CONCLUSIONS: HRT showed beneficial effects by decreasing fibrinolytic activity related to surgically-induced menopause. Short-term HRT treatment was associated with a shift in the procoagulant-anticoagulant balance toward a procoagulant state.
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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) | 0.001 | 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) | 0.001 | 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".