Levels of pregnancy-associated plasma protein A in patients with coronary artery disease
Bibliographic record
Abstract
PURPOSE: To investigate the levels of pregnancy-associated plasma protein A (PAPP-A) or insulin-like growth factor -1 (IGF-1) in patients with acute coronary syndrome. METHODS: Serum PAPP-A and IGF-1 was measured with biotin-tyramide-amplified enzyme immunoassay and Enzyme Linked Immuoserbent Assay, respectively, in patients with ST elevation acute myocardial infarction (STEMI, n=12), unstable angina (UAP, n=15), and stable angina (n=15). PAPP-A and IGF-1 was also measured in 16 healthy subjects (control group). RESULTS: The serum levels of PAPP-A in the STEMI (16.9+/-10.3 mIU/L) and UAP group (15.2+/-10.5 mIU/L) were higher than in the stable angina (8.5+/-3.1 mIU/L) or control group (8.4+/-2.0 mIU/L, P < 0.01). The serum levels of IGF-1 in the STEMI (132.3+/-40.9 microg/L) and UAP group (127.3+/-36.0 microg/L) were also higher than in the stable angina (44.9+/-18.5 microg/L) or control group (67.7+/-24.5microg/L, P < 0.01). There were no differences in serum levels of PAPP-A or IGF-1 among the single, double and three vessel lesion groups. The serum levels of PAPP-A (19.9+/-10.1 mIU/L) and IGF-1 (153.2+/-52.4 microg/L) after PCI were higher than those before PCI (15.1+/-10.0 mIU/L and 91.4+/-51.0 microg/L, respectively, P < 0.01). A positive correlation was found between PAPP-A and IGF-1 levels in the STEMI and UAP group before PCI (r=0.48P < 0.01). CONCLUSION: PAPP-A and IGF-1 are elevated in patients with acute coronary syndrome. They may be used as biomarkers for vulnerable plaques in patients with coronary artery disease. Whether post-PCI elevation of IGF-1 can be used to predict restenosis of coronary arteries remains to be seen.
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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 | 0.000 | 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 | 0.000 |
| 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".