Unraveling the role of miRNAs as biomarkers in Chagas cardiomyopathy: Insights into molecular pathophysiology
Bibliographic record
Abstract
BACKGROUND: Chagas cardiomyopathy (ChCM) is a severe form of Chagas disease and a major cause of cardiovascular morbidity and mortality. The dysregulation of the immune response leads to cardiac remodeling and functional disruptions, resulting in life-threatening complications. Conventional diagnostic methods have limitations, and therapeutic response evaluation is challenging. MicroRNAs (miRNAs), important regulators of gene expression, show potential as biomarkers for diagnosis and prognosis. AIM: This review aims to summarize experimental findings on miRNA expression in ChCM and explore the potential of these miRNAs as biomarkers of Chagas disease. METHODS: The search was conducted in the US National Library of Medicine MEDLINE/PubMed public database using the terms "Chagas cardiomyopathy" OR "Chagas disease" AND "microRNA" OR "miRNA" OR "miR." Additionally, bioinformatics analysis was performed to investigate miRNA-target interactions and explore enrichment pathways of gene ontology biological processes and molecular functions. RESULTS: The miR-21, miR-146b, miR-146a, and miR-155 consistently exhibited up-regulation, whereas miR-145 was down-regulated in ChCM. These specific miRNAs have been linked to fibrosis, immune response, and inflammatory processes in heart tissue. Moreover, the findings from various studies indicate that these miRNAs have the potential as biomarkers for the disease and could be targeted in therapeutic strategies for ChCM. CONCLUSION: In this review, we point out miR-21, miR-146b, miR-146a, miR-155, and miR-145-5p role in the complex mechanisms of ChCM. These miRNAs have been shown as potential biomarkers for precise diagnosis, reliable prognostic evaluation, and effective treatment strategies in the ChCM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".