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Record W2559966496 · doi:10.25011/cim.v39i6.27528

Galectin-3: A biochemical marker to detect paroxysmal atrial fibrillation?

2016· article· en· W2559966496 on OpenAlexvenueno aff
Yusuf Selçoki, Hakan Aydın, Tuğrul Çelik, Ahmet İşleyen, Ali Erayman, Muhammed Bora Demirçelik, Hilmi Demirin, Aydın Köşüş, Beyhan Eryonucu

Bibliographic record

VenueClinical and investigative medicine · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsnot available
Fundersnot available
KeywordsGalectin-3MedicineInternal medicineParoxysmal atrial fibrillationAtrial fibrillationCardiologyFibrosisMyocardial fibrosis

Abstract

fetched live from OpenAlex

PURPOSE: Atrial fibrillation (AF) is the most common form of arrhythmia. AF leads to electrical remodelling and fibrosis of the atria; however, the mechanism(s) remain poorly understood. Galectin-3 is a potential mediator of cardiac fibrosis. The present study aimed to examine the relationship between serum galectin-3 levels and paroxysmal AF. METHODS: Forty-six patients with paroxysmal AF and preserved left ventricular systolic function, and 38 age- and gender-matched control subjects, were involved in the study. Serum galectin-3 levels were analyzed with an enzyme-linked immunosorbent assay (ELISA). RESULTS: Serum galectin-3 levels (median 1.38 ng/mL; 1.21 ng/mL-1.87 ng/mL; p< 0.001) were significantly elevated in patients with paroxysmal AF compared with the control. Left atrial diameter was significantly higher in patients with paroxysmal AF (41.2±3.0 mm vs. 39.6±3.3 mm). Left atrial diameter was found to be significantly correlated with serum galectin-3 levels in patients with paroxysmal AF (r= 0.378, p= 0.001). CONCLUSION: Serum galectin-3 levels are significantly elevated and significantly correlated with left atrial diameter in patients with paroxysmal AF.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.085
GPT teacher head0.334
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2016
Admission routes1
Has abstractyes

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