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Dataset_EV layerparameter

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IEEE2026-04-17 收录
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https://ieee-dataport.org/documents/datasetev-layerparameter
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Extracellular vesicles (EVs) have been recognized as nanocarriers of disease biomarkers. Large EVs (lEV) and small EVs (sEV) are distinct EVs with different size ranges, biogenesis pathways, and biomarker cargoes. Current methods for EV size measurement, including nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM), have limitations in cost, time, and sample handling. This study introduces a novel approach using a shear horizontal surface acoustic wave (SH-SAW) biosensor to estimate the average EV size and the abundance of lEV, independent of the EV concentration.  As the fractional changes in attenuation and velocity for SAW have different scalings with respect to the EV size but the same with respect to concentration, a layer parameter (lp) corresponding to the ratio of amplitude attenuation-to-velocity fractional change (V0ΔA/A0ΔV), is used to remove the EV concentration effect and isolate the dependence on the average size. Uniformly sized Au nanoparticle suspensions are used to establish the linear scaling of lp with respect to particle size, such that lp can estimate the average nanoparticle size within 5 nm. For EV suspensions with complex size distributions, an EV size resolution of about 10 nm is estimated from lp measurements. Separate pulldowns with sEV tetraspanin marker CD9 and generic sEV/lEV marker GPC1 show significant differences in lp for adipose-derived stem cells (ADSCs) and Wharton’s jelly mesenchymal stem cells (WJMSCs), consistent with their distinctly different unimodal and bimodal size distributions from NTA estimates. WJMSC EVs are hence predicted to overexpress lEV. A key to our protocol is to avoid tangential flow filtration, which introduces cell culture-dependent viscosity change that affects SAW characterization.  These results suggest that the upregulation of lEV can be detected with our SH-SAW approach, independent of the EV concentration.
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CHENG, CHIA-HSUAN
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