Insights into the Role of Galectin-3 as a Diagnostic and Prognostic Biomarker of Atrial Fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is an irregular atrial activity and the most prevalent type of arrhythmia. Although AF is easily diagnosed with an electrocardiogram, there is a keen interest in identifying an easy-to-dose biomarker that can predict the prognosis of AF and its recurrence. Galectin-3 (Gal-3) is a beta-galactoside binding protein from the lectin family with pro-fibrotic and -inflammatory effects and a pivotal role in a variety of biological processes, cell proliferation, and differentiation; therefore, it is implicated in the pathogenesis of many cardiovascular (e.g., heart failure (HF)) and noncardiovascular diseases. However, its specificity and sensitivity as a potential marker in AF patients remain debated and controversial. This article comprehensively reviewed the evidence regarding the interplay between Gal-3 and patients with AF. Clinical implications of measuring Gal-3 in AF patients for diagnosis and prognosis are mentioned. Moreover, the role of Gal-3 as a potential biomarker for the management of AF recurrence is investigated. The association of Gal-3 and AF in special populations (coronary artery disease, HF, metabolic syndrome, chronic kidney disease, and diabetes mellitus) has been explored in this review. Overall, although further studies are needed to enlighten the role of Gal-3 in the diagnosis and treatment of AF, our study demonstrated the high potential of this molecule to be used and focused on by researchers and clinicians.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".