ReviewCells2026
Artificial Intelligence for Exosomal Biomarker Discovery for Cardiovascular Diseases: Multi-Omics Integration, Reproducibility, and Translational Prospects.
Review in Cells, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Extracellular Vesicles in Cardiovascular Disease: Intercellular Signaling, Liquid Biopsy Biomarkers, and Therapeutic Translation.Circulation research · 2026Review
- Artificial Intelligence-Enabled Exosomes in Precision Oncology: A Framework for Clinical Utility and Biomedical Applications.Current issues in molecular biology · 2026Review
- Artificial Intelligence-Enabled Bioengineering of Extracellular Vesicle Platforms in Cardiovascular Medicine.Bioengineering (Basel, Switzerland) · 2026Review
- Review
- Molecular Stability as a Translational Gate: A Structured Framework for Target Validation in Genetic Cardiomyopathy.Cureus · 2026Review
- Extracellular Vesicles as Biomarkers for Vascular Disease.Biomolecules · 2026Review
- Review
- An updated review on the role of extracellular vesicles in immune system modulation in breast cancer with special emphasis on immune checkpoint regulators.Frontiers in immunology · 2026Review
- Cardiac biomarkers in athletes and active individuals: a clinical review of exercise-induced elevations and diagnostic interpretation.Frontiers in physiology · 2026Review
- Controversies in the management of asymptomatic carotid stenosis: from best medical therapy to a redefinition of surgical indications.Frontiers in neurology · 2026Review
- Patient-specific digital twins in aortic disease: integrating computational hemodynamics, immune profiling, and precision endovascular strategy.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exosomes and other extracellular vesicles (EVs) carry microRNAs, proteins, and lipids that reflect cardiovascular pathophysiology and can enable minimally invasive biomarker discovery. However, EV datasets are highly dimensional and heterogeneous, strongly influenced by pre-analytic variables and non-standardized isolation/characterization workflows, limiting reproducibility across studies. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and network-based approaches, can support EV biomarker development by integrating multi-omics profiles with clinical metadata. These approaches enable feature selection, disease subtyping, and interpretable model development. Among the AI approaches evaluated, ensemble methods (Random Forest, gradient boosting) demonstrate the most consistent performance for EV biomarker classification (AUC 0.80-0.92), while graph neural networks (GNNs) are particularly promising for path integration but require larger validation cohorts. Evolutionary neural networks applied to EV morphological features yield comparable discrimination but face interpretability challenges for clinical use. Current studies report promising discrimination performance for selected EV-derived panels in acute myocardial infarction and heart failure. However, most evidence remains exploratory, based on small cohorts (
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.