ArticleCommunications medicine2025
Machine learning-assisted assessment of extracellular vesicles can monitor cellular rejection after heart transplant.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Non-invasive imaging in heart transplant recipients: state of art and future direction.European journal of nuclear medicine and molecular imaging · 2026Review
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Serial Cardiac Troponin I Kinetics Do Not Improve Detection of Acute Cellular Rejection Beyond Concurrent Troponin Levels After Heart Transplantation.Journal of clinical medicine · 2026Article
- Extracellular Vesicles in Cardiovascular Disease: Intercellular Signaling, Liquid Biopsy Biomarkers, and Therapeutic Translation.Circulation research · 2026Review
- Artificial intelligence virtual extracellular vesicles (AIVEVs).Bioactive materials · 2026Review
- Noninvasive Biomarkers for Cardiac Allograft Rejection Monitoring: Advances, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Tumor-derived extracellular vesicles: Bridging communication and next-generation theranostics.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2025Review
- Recent progress of nanomaterials for diagnosis and treatment of rejection in heart transplantation.Frontiers in bioengineering and biotechnology · 2025Review
- Advances in the role of extracellular vesicles in circulating microRNA biomarker discovery for lung cancer.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
Abstract
backgroundHeart transplant rejection, particularly acute cellular rejection (ACR), remains a critical post-operative concern, despite declining incidence rates. Current diagnostic standards rely on invasive endomyocardial biopsy, which presents limitations in sensitivity and reproducibility. There is an unmet need for noninvasive, accurate biomarkers that can detect and monitor rejection. This study aims to evaluate whether extracellular vesicle (EV) surface antigens, analyzed through flow cytometry and interpreted with artificial intelligence (AI), can serve as reliable biomarkers for ACR detection and monitoring in heart transplant recipients.
methodsWe conducted a prospective longitudinal cohort study involving 24 heart transplant recipients over a median follow-up of 303 days. A total of 285 blood samples were analyzed for EV surface antigens exploiting two flow cytometry-based protocols. An adaptive AI model (random forest regressor) was developed to interpret EV antigen profiles, dynamically calibrating thresholds per patient.
resultsHere we show that 14 EV surface antigens progressively increase with ACR severity. These changes are evident even before histological diagnosis. The AI model achieves an accuracy of 93.3% at leave-one-out testing (AUC 0.968), and 78.9% at validation in an independent cohort (AUC 0.832), with high specificity and negative predictive value. EV profiling outperforms conventional biochemical markers and provides anticipatory insight into rejection dynamics.
conclusionsEV profiling, enhanced by patient-specific AI modeling, offers a powerful noninvasive method for early detection and monitoring of ACR. This approach holds the potential to reduce reliance on biopsies and tailor immunosuppressive strategies more precisely.
Identifiers
What OpenQuestion 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 OpenQuestion graph.