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Frontiers in Cardiovascular Medicine · 2021 · IF 6.05

Plasma Metabolites Alert Patients With Chest Pain to Occurrence of Myocardial Infarction

Aa Nan,Lu Ying,Yu Mengjie,Tang Heng,Lu Zhenyao,Sun Runbing,Wang Liansheng,Li Chunjian,Yang Zhijian,Aa Jiye,Kong Xiangqing,Wang Guangji

Myocardial infarction (MI) is one of the leading causes of death worldwide, and knowing the early warning signs of MI is lifesaving. To expand our knowledge of MI, we analyzed plasma metabolites in MI and non-MI chest pain cases to identify markers for alerting about MI occurrence based on metabolomics. A total of 230 volunteers were recruited, consisting of 146 chest pain patients admitted with suspected MI (85 MIs and 61 non-MI chest pain cases) and 84 control individuals. Non-MI cardiac chest pain cases include unstable angina (UA), myocarditis, valvular heart diseases, etc. The blood samples of all suspected MI cases were collected not longer than 6 h since the onset of chest pain. Gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry were applied to identify and quantify the plasma metabolites. Multivariate statistical analysis was utilized to analyze the data, and principal component analysis showed MI could be clearly distinguished from non-MI chest pain cases (including UA and other cases) in the scores plot of metabolomic data, better than that based on the data constructed with medical history and clinical biochemical parameters. Pathway analysis highlighted an upregulated methionine metabolism and downregulated arginine biosynthesis in MI cases. Receiver operating characteristic curve (ROC) and adjusted odds ratio (OR) were calculated to evaluate potential markers for the diagnosis and prediction ability of MI (MI vs. non-MI cases). Finally, gene expression profiles from the Gene Expression Omnibus (GEO) database were briefly discussed to study differential metabolites' connection with plasma transcriptomics. Deoxyuridine (dU), homoserine, and methionine scored highly in ROC analysis (AUC > 0.91), sensitivity (>80%), and specificity (>94%), and they were correlated to LDH and AST (p < 0.05). OR values suggested, after adjusting for gender, age, lipid levels, smoking, type II diabetes, and hypertension history, that high levels of dU of positive logOR = 3.01, methionine of logOR = 3.48, and homoserine of logOR = 1.61 and low levels of isopentenyl diphosphate (IDP) of negative logOR = −5.15, uracil of logOR = −2.38, and arginine of logOR = −0.82 were independent risk factors of MI. Our study highlighted that metabolites belonging to pyrimidine, methionine, and arginine metabolism are deeply influenced in MI plasma samples. dU, homoserine, and methionine are potential markers to recognize MI cases from other cardiac chest pain cases after the onset of chest pains. Individuals with high plasma abundance of dU, homoserine, or methionine have increased risk of MI, too.

研究领域: 医学 · 生物化学 · 心血管病学
✓ 已验证 DOI PubMed 高影响因子
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ANALYST · 2021 ·Vol. 146 ·期 24 ·7702-7709 · IF 4.616

5G-Enabled intelligent construction of a chest pain center with up-conversion lateral flow immunoassay

Lei Huang,Shulin Tian,Wenhao Zhao,Ke Liu,Xing Ma,Jinhong Guo,Ming Yin

Acute myocardial infarction (AMI) has become a worldwide health problem because of its rapid onset and high mortality. Cardiac troponin I (cTnI) is the gold standard for diagnosis of AMI, and its rapid and accurate detection is critical for early diagnosis and management of AMI. Using a lateral flow immunoassay with upconverting nanoparticles as fluorescent probes, we developed an up-conversion fluorescence reader capable of rapidly quantifying the cTnI concentration in serum based upon the fluorescence intensity of the test and control lines on the test strip. Reliable detection of cTnI in the range 0.1–50 ng mL−1 could be achieved in 15 min, with a lower detection limit of 0.1 ng mL−1. The reader was also adapted for use on a 5th generation (5G) mobile network enabled intelligent chest pain center. Through Bluetooth wireless communication, the results achieved using the reader on an ambulance heading to a central hospital could be transmitted to a 5G smartphone and uploaded for real-time edge computing and cloud storage. An application in the 5G smartphone allows users to upload their medical information to establish dedicated electronic health records and doctors to monitor patients’ health status and provide remote medical services. Combined with mobile internet and big data, the 5G-enabled intelligent chest pain center with up-conversion lateral flow immunoassay may predict the onset of AMI and save valuable time for patients suffering an AMI.

研究领域: 远程医疗 · 医学信息学 · 心脏病学 · 生物医学工程 · 纳米技术 · 移动健康
✓ 已验证 DOI PubMed 高影响因子
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