Insights into the Role of Galectin-3 as a Diagnostic and Prognostic Biomarker of Atrial Fibrillation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 it