Diagnosis and prognosis of myocardial infarction on a plasmonic chip

Xu, Wei; Wang, Lin; Dai, Hongjie; Sun, Xuming; Huang, Lin; Su, Housheng; Wei, Xunbin; Chen, Chia Chun; Lou, Jiatao; Dai, Hongjie; Qian, Kun

doi:10.1038/s41467-020-15487-3

Cited by 95 publications

(72 citation statements)

References 67 publications

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“…Metabolic serum analysis is more distal over genomic and proteomic approaches for precision diagnostics 11 – 13 , but it has rarely been reported or studied for complex diseases such as LA, due to the lack of efficient metabolite detection tools and systematically designed patient sub-groups. Changes in metabolism are associated with diverse diseases including LA 6 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

et al. 2020

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Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

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Section: Introductionmentioning

confidence: 99%

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

et al. 2020

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“…Enormous studies are dedicated to the issue of MI occurrence in IHD patients, particularly early MI diagnostic [15][16][17][18][19]. However, there is limited data regarding risk factors for UA transformation toward MI [20].…”

Section: Discussionmentioning

confidence: 99%

Blood Plasma Serotonin and von Willebrand Factor as Biomarkers of Unstable Angina Progression Toward Myocardial Infarction

et al. 2021

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Aim: To investigate the serotonin and von Willebrand factor (vWF) concentrations among unstable angina (UA) patients without and with progression toward myocardial infarction (outcome) and to assess the utility of both as prognostic markers of UA complications. Materials and methods: In observational cohort study, we recruited 103 patients with ischemic heart disease (the median age 65.0 (59.0-69.0) years, 45 females (43.7%)). After full set of investigations including high sensitive Troponin I test and 28-day follow-up period, we defined three groups: Group 1 - stable angina patients (n=22) as control, Group 2 - UA patients without outcome (n=71), Group 3 - UA patients with outcome (n=10). We analyzed the blood plasma serotonin content by the ion-exchange chromatography with measurement of serotonin on fluorescence spectrophotometer. VWF concentration was determined by ELISA. We compared the concentrations of observed parameters among the groups with the Kruskal-Wallis test (with post-hoc Mann-Whitney test with Bonferroni-Holm correction). We assessed binary logistic models, receiver operating characteristic curves, calculated sensitivity (Se), specificity (Sp), and positive likelihood ratio (LR+) for each indicator. Results: We registered elevation in serotonin concentration and decline in vWF concentration in Group 3 in comparison with Group 2 (22.670 (20.687-24.927) μg/ml vs 11.980 (8.120-15.000) μg/ml, p< 0.001, and 0.117 (0.109-0.120) rel.units/ml vs 0.134 (0.127-0.143) rel.units/ml, p < 0.001) and Group 1 (12.340 (10.052-13.619) μg/ml, p < 0.001, and 0.137 (0.127-0.156) rel.units/ml, p < 0.001), respectively. No significant differences in serotonin and vWF concentrations between Group 1 and Group 2 were detected (p=0.81 and p=0.36, respectively). The probability of outcome increased significantly (by 60.7% and 59.7%, LR+ 19.0 [6.0, 60.0] and 18.0 [3.9, 80.0]) if serotonin concentration was above 21.575 μg/ml (Se=80.0%, Sp=95.8%, AUC=0.975) and vWF concentration was below 0.114 rel.units/ml (Se=50.0%, Sp=97.2%, AUC=0.973), respectively. Conclusions: Serotonin and vWF as biomarkers are demonstrated promising results for rule-in the patients with risk of short-term UA progression toward myocardial infarction.

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“…Owing to the plasmonic and alloying effects, MPs@SiO 2 @Pd-Au may contribute to efficient electron transfer and high surface stability under laser irradiation during the laser desorption/ionization process [155]. Other authors described the design of a plasmonic gold nano-island (pGold) chip assay for enhanced diagnosis and monitoring of myocardial infarction [156]. A multifunctional platinum nanoreactor intended for point-of-care metabolic analysis, visual detection, and mass spectrometry fingerprinting for in vitro pancreatic cancer diagnostics was designed using controlled core-shell structured Fe 3 O 4 @SiO 2 @Pt particles [157].…”

Section: Advances In Plasmonic Nanomaterialsmentioning

confidence: 99%

Smart Nanomaterials for Biomedical Applications—A Review

Aflori¹

2021

Nanomaterials

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Recent advances in nanotechnology have forced the obtaining of new materials with multiple functionalities. Due to their reduced dimensions, nanomaterials exhibit outstanding physio-chemical functionalities: increased absorption and reactivity, higher surface area, molar extinction coefficients, tunable plasmonic properties, quantum effects, and magnetic and photo properties. However, in the biomedical field, it is still difficult to use tools made of nanomaterials for better therapeutics due to their limitations (including non-biocompatible, poor photostabilities, low targeting capacity, rapid renal clearance, side effects on other organs, insufficient cellular uptake, and small blood retention), so other types with controlled abilities must be developed, called “smart” nanomaterials. In this context, the modern scientific community developed a kind of nanomaterial which undergoes large reversible changes in its physical, chemical, or biological properties as a consequence of small environmental variations. This systematic mini-review is intended to provide an overview of the newest research on nanosized materials responding to various stimuli, including their up-to-date application in the biomedical field.

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Diagnosis and prognosis of myocardial infarction on a plasmonic chip

Cited by 95 publications

References 67 publications

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

Blood Plasma Serotonin and von Willebrand Factor as Biomarkers of Unstable Angina Progression Toward Myocardial Infarction

Smart Nanomaterials for Biomedical Applications—A Review

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