Investigating the Effects of Sex Hormones on Macrophage Polarization
Bibliographic record
Abstract
Sex differences in the development and progression of cardiovascular disease are well established, but the effects of sex hormones on macrophage polarization and pro-atherogenic functions are not well described. We hypothesize that sex hormones directly modulate macrophage polarization, and thereby regulate the progression of atherosclerosis. Bone marrow-derived monocytes from adult male and female C57BL/6 mice were differentiated into macrophages using macrophage colony-stimulating factor (20 ng/mL) and pre-treated with either 17β-estradiol (100 nM), testosterone (100 nM), or a vehicle control for 24 h. Macrophages were polarized into pro- or anti-inflammatory phenotypes and the effects of sex hormone supplementation on the gene expression of macrophage phenotypic markers were assessed using RT-qPCR. Inflammatory markers, including IL-1β, were quantified using an addressable laser bead immunoassay. A transwell migration assay was used to determine changes in macrophage migration. Sex differences were observed in macrophage polarization, inflammatory responses, and migration. Pre-treatment with 17β-estradiol significantly impaired the gene expression of inflammatory markers and the production of IL-1β in inflammatory macrophages. In anti-inflammatory macrophages, 17β-estradiol significantly upregulated the expression of anti-inflammatory markers and enhanced migration. Pre-treatment with testosterone enhanced anti-inflammatory mRNA expression and impaired the production of IL-1β. Our observations suggest a protective role of 17β-estradiol in atherogenesis that may contribute to the sexual dimorphisms in cardiovascular disease observed in human patients.
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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) | 0.001 | 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".