Abstract

Cardiovascular disease is the leading cause of death in Western civilization. In this pilot study we evaluated a new method for the diagnosis of myocardial infarction and heart failure by determining the typical fingerprint in the infrared (IR) spectrum of 1 μL of a dried patient serum sample by Fourier transform IR spectroscopy. For classification, cluster analysis and artificial neural networks (ANN) were applied. In this study 567 subjects were enrolled, comprising 225 controls (Co) and 342 patients with myocardial infarction (MI) (<i>n</i> = 157) and heart failure (HF) (<i>n</i> = 185). By applying artificial neural network algorithms, the following sensitivities and specificities of the same spectra were determined: MI versus Co (98%, 97%), HF versus Co (98%, 100%), MI versus HF (100%, 100%), and MI plus HF versus Co (100%, 100%). Based on our data, mid-IR spectroscopy appears to be a promising new method to diagnose heart diseases from serum samples. Artificial neural network algorithms proved to be superior to cluster analysis for correct prediction.

Application of time-resolved autofluorescence to label-free in vivo optical mapping of changes in tissue matrix and metabolism associated with myocardial infarction and heart failure

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Biomechanical assessment of myocardial infarction using optical coherence elastography

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In vivo label-free optical monitoring of structural and metabolic remodeling of myocardium following infarction

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Characterization of fibrillar collagen isoforms in infarcted mouse hearts using second harmonic generation imaging

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Noninvasive liver diseases detection based on serum surface enhanced Raman spectroscopy and statistical analysis

Xiaozhou Li, Tianyue Yang, Siqi Li, Lili Jin, Deli Wang, Dagang Guan, and Jianhua Ding
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