Anti-N-homocysteinylated protein autoantibodies and cardiovascular disease
Bibliographic record
Abstract
Metabolic conversion of homocysteine (Hcy) to a chemically reactive metabolite, Hcy-thiolactone, catalyzed by methionyl-tRNA synthetase is the first step in a pathway that is suggested to contribute to Hcy toxicity in humans. The accumulation of Hcy-thiolactone is detrimental because of its intrinsic ability to modify proteins by forming N-Hcy-protein adducts, in which a carboxyl group of Hcy is N-linked to the epsilon-amino group of a protein lysine residue. N-linked Hcy occurs in each protein examined and constitutes a significant pool of Hcy in the blood. N-Hcy proteins are likely to be recognized as neo-self antigens and induce an autoimmune response. Indeed, we found that autoantibodies specific for an Nepsilon-Hcy-Lys epitope on N-Hcy-proteins occur in humans. Serum levels of anti-N-Hcy-protein autoantibodies are directly correlated with plasma tHcy, but not with plasma cysteine or methionine levels. In a group of male patients with stroke, levels of anti-N-Hcy-protein autoantibodies and tHcy were significantly higher than in a group of healthy subjects. In a group of male patients with angiographically documented coronary artery disease, seropositivity for anti-N-Hcy-protein autoantibodies occurred five-fold more frequently than in controls and was an independent predictor of coronary artery disease. These findings show that the formation of N-Hcy-proteins has important physiological consequences and support a hypothesis that N-Hcy-protein is a neo-self antigen that contributes to immune activation, an important modulator of atherogenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".