Role of Anti-Beta-1-Adrenergic Receptor Antibodies in Cardiac Dysfunction in Patients with Cirrhotic Cardiomyopathy
Bibliographic record
Abstract
Abstract Background and aim: Cirrhotic cardiomyopathy (CCM) is a recognized complication of cirrhosis and is associated with poor outcomes, especially under challenges such as surgery/liver transplantation. However, the mechanism is not clear, and the treatment is not specific. The present study aimed to evaluate the role of anti-β1-adrenergic receptor antibodies (anti-β1-AR) in CCM.Methods: We enrolled 3 groups: healthy controls, cirrhotic patients without CCM and patients with CCM. The serum anti-β1-AR and N-terminal (NT)-pro hormone brain natriuretic peptide (NT-proBNP) were detected by enzyme-linked immunosorbent assay (ELISA). Left ventricular ejection fraction (LVEF), fractional shortening (FS), the ratio of peak early (E wave) and atrial (A wave) flow velocities (E/A) and left ventricular posterior wall minor motion amplitude were measured by echocardiography. Results: The anti-β1-AR levels in the CCM group were significantly higher than that in the non-CCM group (0.97±0.37 vs 0.74±0.37 ng/mL, P<0.05); anti-β1-AR was positively correlated to NT-proBNP (r=0.48, P < 0.05), negatively correlated to LVEF (r = - 0.466, P < 0.05), FS (r = - 0.488, P < 0.05) and E/A (r=-0.475, P < 0.05) in CCM patients. The area under the receiver-operating-characteristic (AUROC) of serum anti-β1-AR on CCM was 0.678 (95% CI 0.515-0.768). At a cutoff value of 0.669 ng/mL, the sensitivity of anti-β1-AR to diagnose CCM was 89.5%, and the specificity was 57%. Conclusion: The anti-β1-AR level in the CCM group was significantly elevated compared with that in the non-CCM group, and this increase was correlated with cardiac function. Anti-β1-AR is a useful predictive biomarker for the presence of CCM and eventually may also have therapeutic implications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".