The biomarker N-terminal pro-brain natriuretic peptide and liver diseases
Bibliographic record
Abstract
PURPOSE: NT-proBNP has emerged as a powerful diagnostic and prognostic biomarker in heart disease. Studies showed that NT-proBNP is a sensitive biomarker for identifying patients with heart failure caused by hepatitis C virus (HCV) related myocarditis. The purpose of this study was to evaluate the correlation between the serum concentration of NT-proBNP and hepatitis virus infection/liver disease. METHODS: 223 serum samples from blood donors (aged 19~50 years old) were collected as a control group, and 644 samples were obtained from patients infected by hepatitis viruses including 493 HBV: 364 chronic hepatitis (CH), 86 hepatocellular carcinoma (HCC) and 43 liver cirrhosis (LC) and 151 HCV (85 CH, 14 HCC, 52 LC). All samples were assayed with an Elecsys immunoassay analyzer for NT-proBNP concentration. RESULTS: The mean concentration of NT-proBNP in the control group was 21.77 pg/ml and showed no significant variation with either age or gender. Both the mean value and the rate of abnormality of NT-proBNP were significantly higher for the HBV- and HCV-infected groups in comparison with the control group. The mean NT-proBNP value (380.24 pg/ml) and abnormality rate (38.41%) in the HCV group were higher than that of the HBV group. For samples from patients with HBV/HCV-related hepatic disease/pathology, the mean NT-proBNP value (517.19 pg/ml/597.18 pg/ml) were the highest in the liver cirrhosis group. CONCLUSIONS: Hepatic pathologic lesions, particularly cirrhosis, may contribute to the elevation of NT-proBNP in subjects with HBV/HCV infection.
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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 | 0.000 | 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".