Hyperhomocysteinemia: a missing link to dysfunctional HDL via paraoxanase-1
Bibliographic record
Abstract
Paraoxanase-1 (PON1) is an HDL-associated enzyme that contributes to the antioxidant and antiatherosclerotic properties of HDL. Lack of PON1 results in dysfunctional HDL. HHcy is a risk factor for cardiovascular disorders, and instigates vascular dysfunction and ECM remodeling. Although studies have reported HHcy during atherosclerosis, the exact mechanism is unclear. Here, we hypothesize that dysfunctional HDL due to lack of PON1 contributes to endothelial impairment and atherogenesis through HHcy-induced ECM re-modeling. To verify this hypothesis, we used C57BL6/J and PON1 knockout mice (KO) and fed them an atherogenic diet. The expression of Akt, ADMA, and DDAH, as well as endothelial gap junction proteins such as Cx-37 and Cx-40 and eNOS was measured for vascular dysfunction and inflammation. We observed that cardiac function was decreased and plasma Hcy levels were increased in PON1 KO mice fed the atherogenic diet compared with the controls. Expression of Akt, eNOS, DDAH, Cx-37, and Cx-40 was decreased, and the expression of MMP-9 and ADMA was increased in PON1 KO mice fed an atherogenic diet compared with the controls. Our results suggest that HHcy plays an intricate role in dysfunctional HDL, owing to the lack of PON1. This contributes to vascular endothelial impairment and atherosclerosis through MMP-9-induced vascular remodeling.
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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 | 0.001 | 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".