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Analytical techniques for detecting micro- and nanoplastics in blood and vascular tissues: Strengths and limitations
Pasquale Iovino1,2, Angelo Fenti3, Marta Belmonte4,5
1Department of Environmental Biological and Pharmaceutical Sciences and Technologies, University of Campania "Luigi Vanvitelli", Caserta, Italy.
Abstract:
Micro- and nanoplastics (MNPs) are increasingly reported in human biofluids and tissues, including cardiovascular (CV)-relevant specimens, making reliable detection and quantification a prerequisite for clinically meaningful research in cardiology. However, human-derived matrices are analytically challenging because they are often available in limited amounts, rich in lipids and proteins, highly susceptible to background contamination, and subject to matrix-driven interferences that can bias polymer identification and quantification, particularly for submicron fractions. This review provides a method-focused overview of the analytical toolbox most frequently used for MNPs assessment in biologically relevant and human samples, with specific attention to CV applications. We compare particle-resolved vibrational approaches (μ-Fourier transform infrared spectroscopy, μ-Raman, and quantum cascade laser-based laser direct infrared spectroscopy) that deliver polymer identification alongside particle counts, size proxies, and morphology, and mass-based strategies (double-shot pyrolysis-gas chromatography/mass spectrometry [Py-GC/MS] and targeted depolymerization coupled to liquid chromatography-MS/MS) that provide polymer-specific mass burdens suited to exposure metrics and clinical correlations. Representative studies are discussed, including recent analyses of atheromatous plaques, coronary blood, thrombi, and other human tissue where polymer burden, morphology, and size have been investigated in relation to adverse health outcomes. Finally, we outline the main advantages and limitations of each technique, emphasizing practical factors that influence data quality and comparability across studies. By framing MNP analytics within clinically relevant CV specimens and endpoints, this review aims to critically appraise current evidence and design robust translational investigations.
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