The effect of HLA-DRB1*04:01 on a mouse model of atherosclerosis
Bibliographic record
Abstract
HLA-DRB1 is associated with an increased risk of cardiovascular disease in patients with rheumatoid arthritis (RA). This study aimed to determine the effect of HLA-DRB1 on atherosclerotic cardiovascular disease (ASCVD) using a novel mouse model. Mice transgenic for HLA-DRB1*04:01 (DR4tg) were crossed with low density lipoprotein receptor knock-out (Ldlr−/−) mice that develop atherosclerosis when fed a high fat, high cholesterol (HFHC) diet. Male and female DR4tgLdlr−/− (n = 48), Ldlr−/− (n = 24), DR4tg (n = 24), and C57Bl/6 (B6) background (n = 24) mice were fed HFHC or regular diet (RD) for 12 weeks. Blood samples were analyzed for serum lipoproteins using a colorimetric assay. C-reactive protein (CRP) and oxidized LDL (OxLDL) were measured using ELISA. Atherosclerosis in the aortas was assessed using the lipid stain, Sudan IV. The presence of citrulline in atherosclerotic plaque was determined by immunohistochemistry. Sera low-density lipoprotein cholesterol (LDL-C) levels were higher in HFHC-fed Ldlr−/− versus DR4tgLdlr−/−-; p = 0.0056, but the aortic plaque burden and degree of citrullination in the plaque were similar for these two strains. The ratio of pro-atherogenic OxLDL to LDL levels was higher in DR4tgLdlr−/− than Ldlr−/−mice; p = 0.0017. All mice had an increase in CRP when fed a HFHC diet, most pronounced for DR4tgLdlr−/−; p = 0.0009. There were no significant sex differences for DR4tgLdlr−/− mice; however, male Ldlr−/− mice had worse atherosclerosis. B6 and DR4tg mice did not have significant elevations in serum cholesterol levels and did not develop atherosclerosis. Expression of HLA-DRB1 resulted in an elevation of OxLDL and a reduction in the male bias for atherosclerosis, mimicking what is observed in RA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".