Acute myocardial infarction(AMI)represents a leading cause of death globally.Key to AMI recovery is timely diagnosis and initiation of treatment,ideally within 3 h of symptom onset.Cardiac troponin T(cTnT)is the gold ...Acute myocardial infarction(AMI)represents a leading cause of death globally.Key to AMI recovery is timely diagnosis and initiation of treatment,ideally within 3 h of symptom onset.Cardiac troponin T(cTnT)is the gold standard yet a low cTnT result cannot rule out AMI at early times.Here,we develop a three-biomarker joint strategy for early and accurate diagnosis of AMI via an electrochemiluminescence(ECL)immunoarray coupled with robust machine learning.The ECL immunoarray is based on an array microchip with a singleelectrode and chemiluminescent immuno-Gold(ciGold)nanoassemblies.The ciGold immunoarray was obtained by successively assembling nanocomposites of Cu^(2+)/cysteine complexes and N-(aminobutyl)-N-(ethylisoluminol)bifunctionalized gold nanoparticles combined with chitosan and antibody conjugated gold nanoparticles on the surface of a microchip.Three biomarkers,including cardiac troponin I,heart type fatty acid binding protein,and copeptin,were simultaneously detected in 260 serum samples from patients presenting with chest pain by an innovative multiplexed ECL immunoarray,and classified via the three-biomarker joint assessment model using support vector machines.The model achieved perfect discrimination(100%sensitivity and specificity)for AMI vs non-AMI patients,substantially higher than cTnT alone.Within 12 h of symptom onset,high-sensitivity cardiac troponin T(hs-cTnT)misclassified>20%of patients,while the joint biomarker assessment model retained perfect accuracy.As the time between symptom onset and testing became shorter,the degree to which the joint assessment model outperformed hs-cTnT increased.The proposed threebiomarker joint strategy is obviously superior to hs-cTnT for early and accurate diagnosis of AMI,hopefully reducing AMI mortality and saving limited medical resources.展开更多
基金support of this research by the National Key Research and Development Program of China(Grant No.2016YFA0201300)the National Natural Science Foundation of China(Grant Nos.21874122 and 21527807)are gratefully acknowledged.
文摘Acute myocardial infarction(AMI)represents a leading cause of death globally.Key to AMI recovery is timely diagnosis and initiation of treatment,ideally within 3 h of symptom onset.Cardiac troponin T(cTnT)is the gold standard yet a low cTnT result cannot rule out AMI at early times.Here,we develop a three-biomarker joint strategy for early and accurate diagnosis of AMI via an electrochemiluminescence(ECL)immunoarray coupled with robust machine learning.The ECL immunoarray is based on an array microchip with a singleelectrode and chemiluminescent immuno-Gold(ciGold)nanoassemblies.The ciGold immunoarray was obtained by successively assembling nanocomposites of Cu^(2+)/cysteine complexes and N-(aminobutyl)-N-(ethylisoluminol)bifunctionalized gold nanoparticles combined with chitosan and antibody conjugated gold nanoparticles on the surface of a microchip.Three biomarkers,including cardiac troponin I,heart type fatty acid binding protein,and copeptin,were simultaneously detected in 260 serum samples from patients presenting with chest pain by an innovative multiplexed ECL immunoarray,and classified via the three-biomarker joint assessment model using support vector machines.The model achieved perfect discrimination(100%sensitivity and specificity)for AMI vs non-AMI patients,substantially higher than cTnT alone.Within 12 h of symptom onset,high-sensitivity cardiac troponin T(hs-cTnT)misclassified>20%of patients,while the joint biomarker assessment model retained perfect accuracy.As the time between symptom onset and testing became shorter,the degree to which the joint assessment model outperformed hs-cTnT increased.The proposed threebiomarker joint strategy is obviously superior to hs-cTnT for early and accurate diagnosis of AMI,hopefully reducing AMI mortality and saving limited medical resources.
