A new design and implementation of smartphone light sheet microscopy based on inkjet-printed DotLens is presented. Its ultimate simplicity, yet high quality imaging and sensing capabilities would find applications in ...
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Plasmonic hybridization in a gold-silver alloy nanodisks array results in a pair of high and low energy LSPR modes. This high energy mode is applied for colorimetric detection of sub-nM and sub-monolayer biotin-strept...
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We present holographic generation of photothermal microbubbles on high-density nanoporous gold array, which allows dynamic control of size and location, and can be used for assembling micro/nanoparticles readily measu...
Sensitive and selective detection and quantitation of small molecules and enzymatic activities have been attempted using surface-enhanced Raman spectroscopy (SERS). Preimmobilized aptamers and in situ assembled aptame...
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Inkjet-printed PMDS lenses have been fabricated and can turn a smartphone into a pocket microscope achieving 1 µm resolution. Multimodal imaging capabilities such as bright-field, darkfield, fluorescence, and nan...
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We report glucose sensing 10 mM to 0.1 mM in water using stamping surface-enhanced Raman spectroscopy (S-SERS) technique with nanoporous gold disk (NPGD) plasmonic substrates, a reagent- and separation-free technique....
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In clinical diagnostics and research involving histopathology, formalin fixed paraffin embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 hours) ...
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Plasmonic arrays fabricated by low-cost nanosphere lithography feature disorderliness and corresponding non-uniform index sensitivity. A calibration technique based on hyperspectral imaging has been implemented to reg...
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Acute myocardial infarction (MI) is complex, and a variety of pathologies can result in this clinical diagnosis. Current guidelines recognize two classes of MI: thrombotic (Type 1) and non-thrombotic (Type 2), which h...
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Acute myocardial infarction (MI) is complex, and a variety of pathologies can result in this clinical diagnosis. Current guidelines recognize two classes of MI: thrombotic (Type 1) and non-thrombotic (Type 2), which have similar prevalence but require different treatment. Unfortunately, diagnostic criteria for differentiating between the two do not exist. This results in inefficient and sub-optimal care of patients suspected of MI. This paper introduces a novel CAD system for categorizing MI by analyzing the association between blood metabolites with the formation of thrombosis as a cause of MI. The data collection stage involves a new non-targeted approach that detects the quantities of both know and unknown metabolites from blood samples. Our system uses a randomized ensemble-learning scheme to increase the model accuracy. This scheme includes a recursive feature elimination (RFE) algorithm to discard weakly correlated metabolites and a multi-classifier algorithm that votes between five different classifiers in order to enhance accuracy. The output of our system includes suggested clusters of metabolites that act as biomarkers to differentiate between Type 1 and Type 2 MI. Our experiment achieves diagnostic accuracy of %94, sensitivity of %90 and specificity of %81.
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