The <i>LPS2</i> mutation in TRIF is atheroprotective in hyperlipidemic low density lipoprotein receptor knockout mice
Bibliographic record
Abstract
Signaling through MyD88, an adaptor utilized by all TLRs except TLR3, is pro-atherogenic; however, it is unknown whether signaling through TIR-domain-containing adaptor-inducing interferon-β (TRIF), an adaptor used only by TLRs 3 and 4, is relevant to atherosclerosis. We determined that the TRIF(Lps2) lack-of-function mutation was atheroprotective in hyperlipidemic low density lipoprotein (LDL) receptor knockout (LDLr(-/-)) mice. LDLr(-/-) mice were crossed with either TRIF(Lps2) or TLR3 knockout mice. After feeding an atherogenic diet for 10-15 wks, atherosclerotic lesions in the heart sinus and aorta were quantitated. LDLr(-/-) mice with TRIF(Lps2) were significantly protected from atherosclerosis. TRIF(Lps2) led to a reduction in cytokines secreted from peritoneal macrophages (M) in response to hyperlipidemia. Moreover, heart sinus valves from hyperlipidemic LDLr(-/-) TRIF(Lps2) mice had significantly fewer lesional M. However, LDLr(-/-) mice deficient in TLR3 showed some enhancement of disease. Collectively, these data suggest that hyperlipidemia resulting in endogenous activation of the TRIF signaling pathway from TLR4 leads to pro-atherogenic events.
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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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".