Effects of raloxifene treatment on the phenotype of blood monocytes
Bibliographic record
Abstract
Raloxifene (RLX), a selective oestrogen receptor modulator, has oestrogen-agonist effects on bone, lipoproteins, and homocysteine and oestrogen-antagonist activity in the breast and uterus, positioning it as a potential drug for long-term prevention of coronary heart disease in postmenopausal women. To further evaluate its influence on cardiovascular risk factors, we studied the effects of 60 mg/day RLX on serum lipid levels, inflammatory (high-sensitivity C-reactive protein, and coagulation (fibrinogen) markers, monocytes, and fibrinolysis in 15 healthy postmenopausal women. Markers were measured at baseline, after 1 month without treatment, and after 3 months of treatment. Fibrinolysis was evaluated using the euglobulin clot lysis time (ECLT) determined with a new semiautomatic optical method. Monocyte phenotype was determined by measurement of the expression of the antigens CD14, HLA-DR, and CD62-L using flow cytometry. After 3 months of RLX treatment, we observed a decrease in total cholesterol (p = 0.002), in low-density lipoprotein cholesterol (p <0.001), and in lipoprotein A (p = 0.01). Fibrinogen (p = 0.002) decreased significantly, and high-sensitivity C-reactive protein had a tendency to decrease, but this did not reach statistical significance (p = 0.06). RLX treatment had no effect on ECLT (p = 0.223) or on white blood cell, lymphocyte, and total monocyte counts (p = 0.313). Monocyte expression of HLA-DR, CD14, and CD62-L was not modified by the treatment. In conclusion, we confirm that RLX has beneficial short-term effects on levels of lipids and inflammatory markers, with no effect on fibrinolysis or monocyte phenotype.
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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 | 0.000 |
| 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".