Evidence map›Paper›PMID 39062199›Full record

ArticleBiomedicines2024

Unveiling the Potential of Migrasomes: A Machine-Learning-Driven Signature for Diagnosing Acute Myocardial Infarction.

Yihao Zhu, Yuxi Chen, Jiajin Xu, Yao Zu

Abstract read
In one paragraph

Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Review
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  5. Integrating computational engines to identifyFrontiers in immunology · 2026
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  13. Deciphering the Regulatory Networks of the Migrasome-Associated Cell Subpopulation in Heterotopic Ossification via Multi-Omics Analysis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025
    Article
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  16. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yihao ZhuInternational Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Yuxi ChenInternational Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Jiajin XuDepartment of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang 212013, China.
Yao ZuInternational Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, China.ORCID 0000-0001-8081-9136

Funding

Chenguang Program from the Shanghai Education Committee 14CG49National Natural Science Foundation of China 31501166National Natural Science Foundation of China 32170423Shanghai Sailing Program of Science and Technology Commission of Shanghai Municipality 15YF1405000
6 · The paper itself

Abstract

backgroundRecent studies have demonstrated that the migrasome, a newly functional extracellular vesicle, is potentially significant in the occurrence, progression, and diagnosis of cardiovascular diseases. Nonetheless, its diagnostic significance and biological mechanism in acute myocardial infarction (AMI) have yet to be fully explored.

methodsTo remedy this gap, we employed an integrative machine learning (ML) framework composed of 113 ML combinations within five independent AMI cohorts to establish a predictive migrasome-related signature (MS). To further elucidate the biological mechanism underlying MS, we implemented single-cell RNA sequencing (scRNA-seq) of cardiac

resultsMS demonstrated robust predictive performance and superior generalization, driven by the optimal combination of Stepglm and Lasso, on the expression of nine migrasome genes (

conclusionsOur MS has implications in forecasting and curving AMI to inform future diagnostic and therapeutic strategies for AMI.

Indexed as

acute myocardial infarctionbioinformaticsdiagnosismachine learningmigrasome

Identifiers

PMID39062199
PMCPMC11274667

What Socratic holds

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Registered trials

None linked

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.