Galectin-3 inhibits cardiac contractility via a tumor necrosis factor alpha-dependent mechanism in cirrhotic rats
Bibliographic record
Abstract
BACKGROUND/AIMS: Galectin-3 plays a key pathogenic role in cardiac hypertrophy and heart failure. The present study aimed to investigate the effects of galectin-3 on cardiomyopathy - related factors and cardiac contractility in a rat model of cirrhotic cardiomyopathy. METHODS: Rats were divided into two sets, one for a functional study, the other for cardiac contractile-related protein evaluation. There were four groups in each set: sham operated and sham plus N-acetyllactosamine (N-Lac, a galectin-3 inhibitor; 5 mg/kg); bile duct ligated (BDL) and BDL plus N-Lac. Four weeks after surgery, ventricular level of galectin-3, collagen I and III ratio, tumor necrosis factor alpha (TNFα), and brain natriuretic peptide (BNP) were measured either by Western blots or immunohistochemistry or enzyme-linked immunosorbent assay. Blood pressure was measured by polygraph recorder. Cardiomyocyte contractility was measured by inverted microscopy. RESULTS: Galectin-3 and collagen I/III ratio were significantly increased in cirrhotic hearts. TNFα and BNP were significantly increased in BDL serum and heart compared with sham controls. Galectin-3 inhibitor significantly decreased galectin-3, TNFα, and BNP in cirrhotic hearts but not in sham controls. N-Lac also significantly improved the blood pressure, and systolic and diastolic cardiomyocyte contractility in cirrhotic rats but had no effect on sham controls. CONCLUSION: Increased galectin-3 in the cirrhotic heart significantly inhibited contractility via TNFα. Inhibition of galectin-3 decreased the cardiac content of TNFα and BNP and reversed the decreased blood pressure and depressed contractility in the cirrhotic heart. Galectin-3 appears to play a pathogenic role in cirrhotic cardiomyopathy.
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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 | 0.000 |
| 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 | 0.001 |
| 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".