{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhu Y"],"funding":["National Natural Science Foundation of China","Shanghai Sailing Program of Science and Technology Commission of Shanghai Municipality","Chenguang Program from the Shanghai Education Committee"],"pagination":["612"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11221097"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["22(1)"],"pubmed_abstract":["<h4>Background</h4>Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.<h4>Methods</h4>We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian"],"journal":["Journal of translational medicine"],"pubmed_title":["Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation."],"pmcid":["PMC11221097"],"funding_grant_id":["31501166","32170423","15YF1405000","14CG49"],"pubmed_authors":["Zhu Y","Chen Y","Zu Y"],"additional_accession":[]},"is_claimable":false,"name":"Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation.","description":"<h4>Background</h4>Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.<h4>Methods</h4>We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-07-03T03:17:34.653Z","creation":"2024-10-16T07:26:13.837Z"},"accession":"S-EPMC11221097","cross_references":{"pubmed":["38956669"],"doi":["10.1186/s12967-024-05415-0"]}}