Adenosine plasma level correlates with homocysteine and uric acid concentrations in patients with coronary artery disease
Bibliographic record
Abstract
The role of hyperhomocysteinemia in coronary artery disease (CAD) patients remains unclear. The present study evaluated the relationship between homocysteine (HCys), adenosine plasma concentration (APC), plasma uric acid, and CAD severity evaluated using the SYNTAX score. We also evaluated in vitro the influence of adenosine on HCys production by hepatoma cultured cells (HuH7). Seventy-eight patients (mean age ± SD: 66.3 ± 11.3; mean SYNTAX score: 19.9 ± 12.3) and 30 healthy subjects (mean age: 61 ± 13) were included. We incubated HuH7 cells with increasing concentrations of adenosine and addressed the effect on HCys level in cell culture supernatant. Patients vs. controls had higher APC (0.82 ± 0.5 μmol/L vs 0.53 ± 0.14 μmol/L; p < 0.01), HCys (15 ± 7.6 μmol/L vs 6.8 ± 3 μmol/L, p < 0.0001), and uric acid (242.6 ± 97 vs 202 ± 59, p < 0.05) levels. APC was correlated with HCys and uric acid concentrations in patients (Pearson's R = 0.65 and 0.52; p < 0.0001, respectively). The SYNTAX score was correlated with HCys concentration. Adenosine induced a time- and dose-dependent increase in HCys in cell culture. Our data suggest that high APC is associated with HCys and uric acid concentrations in CAD patients. Whether the increased APC participates in atherosclerosis or, conversely, is part of a protective regulation process needs further investigations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".