Effects of LDL Receptor Modulation on Lymphatic Function
Bibliographic record
Abstract
Atherosclerosis is driven by the accumulation of immune cells and cholesterol in the arterial wall. Although recent studies have shown that lymphatic vessels play an important role in macrophage reverse cholesterol transport, the specific underlying mechanisms of this physiological feature remain unknown. In the current report, we sought to better characterize the lymphatic dysfunction that is associated with atherosclerosis by studying the physiological and temporal origins of this impairment. First, we assessed that athero-protected Pcsk9(-/-) mice exhibited improved collecting lymphatic vessel function throughout age when compared to WT mice for up to six months, while displaying enhanced expression of LDLR on lymphatic endothelial cells. Lymphatic dysfunction was present before the atherosclerotic lesion formation in a mouse model that is predisposed to develop atherosclerosis (Ldlr(-/-); hApoB100(+/+)). This dysfunction was presumably associated with a defect in the collecting lymphatic vessels in a non-specific cholesterol- but LDLR-dependent manner. Treatment with a selective VEGFR-3 agonist rescued this impairment observed early in the onset of this arterial disease. We suggest that LDLR modulation is associated with early atherosclerosis-related lymphatic dysfunction, and bring forth a pleiotropic role for PCSK9 in lymphatic function. Our study unveils new potential therapeutic targets for the prevention and treatment of atherosclerosis.
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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) | 0.002 | 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".