ArticleCells2026
Identifying Platelet Lipidomic Networks and Evaluating Machine-Learning Models to Identify Distinctive Features Between Chronic and Acute Coronary Syndrome.
Vivek Nandhan Kanpa, Suzy Whoriskey, Ana Le Chevillier, Jean de Villiers, Adrian Brun, Tobias Harm, Manuel Sigle, Kristina Dittrich, Andreas Goldschmied, Dominik Rath and 4 more
Abstract read
In one paragraphArticle in Cells, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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0citing papers in PubMed
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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 registryThe 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.
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3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
14 authors.
Vivek Nandhan KanpaUCD Conway SPHERE Research Group, Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland.ORCID 0000-0001-6002-2170 Ana Le ChevillierUCD Conway SPHERE Research Group, Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland.
Jean de VilliersSAS Institute Ireland, 2 Windmill Lane, Dublin Docklands, D02 K156 Dublin, Ireland.
Adrian BrunInstitute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, 72076 Tübingen, Germany.ORCID 0009-0000-6107-4642 Tobias HarmDepartment of Cardiology and Angiology, University Hospital Tübingen, Eberhard Karls University Tübingen, 72074 Tübingen, Germany.
Manuel SigleDepartment of Cardiology and Angiology, University Hospital Tübingen, Eberhard Karls University Tübingen, 72074 Tübingen, Germany.
Kristina DittrichInstitute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, 72076 Tübingen, Germany.
Andreas GoldschmiedDepartment of Cardiology and Angiology, University Hospital Tübingen, Eberhard Karls University Tübingen, 72074 Tübingen, Germany.
Dominik RathDepartment of Cardiology and Angiology, University Hospital Tübingen, Eberhard Karls University Tübingen, 72074 Tübingen, Germany.
Michael LämmerhoferInstitute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, 72076 Tübingen, Germany.ORCID 0000-0002-1318-0974 Patricia B MaguireUCD Conway SPHERE Research Group, Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland.ORCID 0000-0002-0362-3778 Meinrad P GawazDepartment of Cardiology and Angiology, University Hospital Tübingen, Eberhard Karls University Tübingen, 72074 Tübingen, Germany.ORCID 0000-0003-1124-9592 Luisa WeissUCD Conway SPHERE Research Group, Conway Institute, University College Dublin, D04 V1W8 Dublin, Ireland.
Funding
Deutsche Forschungsgemeinschaft 335549539 / GRK 2381Deutsche Forschungsgemeinschaft 374031971 / TRR 240Deutsche Forschungsgemeinschaft INST 37/1318-1 FUGGEnterprise Ireland DT 2021 0373AEuropean Commission 101203749Science Foundation Ireland 19/FIP/AI/7490R2
6 · The paper itselfAbstract
Platelet lipidomics offers a window into the thromboinflammatory responses to acute coronary syndromes (ACS) and chronic coronary syndromes (CCS), yet differences between the platelet lipidomes of these two coronary artery disease subtypes remain poorly characterized. In this pilot study, untargeted platelet lipidomics and miRNA transcriptomics were performed on platelets isolated from 19 ACS and 57 CCS patients undergoing coronary angiography, integrated with routine clinical and hematological traits in statistical network analyses and cross-validated machine learning models. Platelet lipidomics identified 81 lipid features significantly altered between ACS and CCS (FDR
Indexed as
Acute Coronary SyndromeBlood PlateletsLipidomicsMachine LearningAgedChronic DiseaseFemaleHumansLipidsMaleMiddle AgedPilot ProjectsLipidscoronary diseasemachine learningmulti-omicsplatelets
Identifiers
PMID42439667
PMCPMC13359697
What OpenQuestion holds
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LicenceCC BY
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