Digital Microfluidic Thermal Control Chip-Based Multichannel Immunosensor for Noninvasively Detecting Acute Myocardial Infarction.
Jienan ShenLiyuan ZhangJunjie YuanYongsheng ZhuHao ChengYibo ZengJiaqin WangXueqiu YouChaoyong James YangXiangmeng QuHong ChenPublished in: Analytical chemistry (2021)
Rapid and automated detection of acute myocardial infarction (AMI) at its developing stage is very important due to its high mortality rate. To quantitatively diagnose AMI, Myo, CK-MB, and cTnI are chosen as three biomarkers, which are usually detected through an immunosorbent assay, such as the enzyme-linked immunosorbent assay. However, the approach poses many drawbacks, such as long detection time, the cumbersome process, the need for professionals, and the difficulty of realizing automatic operation. Here, a multichannel digital microfluidic (DMF) thermal control chip integrated with a sandwich-based immunoassay strategy is proposed for the automated, rapid, and sensitive detection of AMI biomarkers. A miniaturized temperature control module is integrated on the back of the DMF chip, meeting the temperature requirement for the immunoassay. With this DMF thermal control chip, sample and reagent consumption are reduced to several microliters, significantly alleviating reagent consumption and sample dependence, and the automated and multichannel detection of biomarkers can be achieved. In this work, the simultaneously noninvasive detection of the human serum sample containing the three biomarkers of AMI is also achieved within 30 min, which improves the diagnostic accuracy of AMI. Due to the features of automation and miniaturization, the multichannel immunosensor can be used in community hospitals to increase the speed of diagnosis of patients with various acute diseases.
Keyphrases
- acute myocardial infarction
- loop mediated isothermal amplification
- sensitive detection
- high throughput
- label free
- percutaneous coronary intervention
- circulating tumor cells
- quantum dots
- left ventricular
- single cell
- deep learning
- healthcare
- machine learning
- real time pcr
- liver failure
- mental health
- cardiovascular disease
- acute coronary syndrome
- coronary artery disease
- intensive care unit
- respiratory failure