Excessive hypercholesterolemia in pregnancy impairs rat uterine artery function via activation of Toll-like receptor 4
Bibliographic record
Abstract
Hypercholesterolemia in pregnancy is a physiological process required for normal fetal development. In contrast, excessive pregnancy-specific hypercholesterolemia increases the risk of complications, such as preeclampsia. However, the underlying mechanisms are unclear. Toll-like receptor 4 (TLR4) is a membrane receptor modulated by high cholesterol levels, leading to endothelial dysfunction; but whether excessive hypercholesterolemia in pregnancy activates TLR4 is not known. We hypothesized that a high cholesterol diet (HCD) during pregnancy increases TLR4 activity in uterine arteries, leading to uterine artery dysfunction. Sprague Dawley rats were fed a control diet (n=12) or HCD (n=12) during pregnancy (gestational day 6-20). Vascular function was assessed in main uterine arteries using wire myography (vasodilation to methacholine and vasoconstriction to phenylephrine; with and without inhibitors for mechanistic pathways) and pressure myography (biomechanical properties). Exposure to a HCD during pregnancy increased maternal blood pressure, induced proteinuria, and reduced the fetal-to-placental weight ratio for both sexes. Excessive hypercholesterolemia in pregnancy also impaired vasodilation to methacholine in uterine arteries, whereby at higher doses, methacholine caused vasoconstriction instead of vasodilation in only the HCD group, which was prevented by inhibition of TLR4 or prostaglandin H synthase 1. Endothelial nitric oxide synthase expression and nitric oxide levels were reduced in HCD compared with control dams. Vasoconstriction to phenylephrine and biomechanical properties were similar between groups. In summary, excessive hypercholesterolemia in pregnancy impairs uterine artery function, with TLR4 activation as a key mechanism. Thus, TLR4 may be a target for therapy development to prevent adverse perinatal outcomes in complicated pregnancies.
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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".