The last pandemic exposed critical gaps in monitoring and mitigating the spread of viral respiratory infections at the point‐of‐need. A cost‐effective multiplexed fluidic device (NFluidEX), as a home‐test kit analogous to a glucometer, that uses saliva and blood for parallel quantitative detection of viral infection and body's immune response in an automated manner within 11 min is proposed. The technology integrates a versatile biomimetic receptor based on molecularly imprinted polymers in a core–shell structure with nano gold electrodes, a multiplexed fluidic‐impedimetric readout, built‐in saliva collection/preparation, and smartphone‐enabled data acquisition and interpretation. NFluidEX is validated with Influenza A H1N1 and SARS‐CoV‐2 (original strain and variants of concern), and achieves low detection limit in saliva and blood for the viral proteins and the anti‐receptor binding domain (RBD) Immunoglobulin G (IgG) and Immunoglobulin M (IgM), respectively. It is demonstrated that nanoprotrusions of gold electrodes are essential for the fine templating of antibodies and spike proteins during molecular imprinting, and differentiation of IgG and IgM in whole blood. In the clinical setting, NFluidEX achieves 100% sensitivity and 100% specificity by testing 44 COVID‐positive and 25 COVID‐negative saliva and blood samples on par with the real‐time quantitative polymerase chain reaction (p < 0.001, 95% confidence) and the enzyme‐linked immunosorbent assay.

Non-invasive liquid biopsies offer hope for a rapid, risk-free, real-time glimpse into cancer diagnostics. Recently, hydrogen peroxide (H2O2) is identified as a cancer biomarker due to continued release from cancer...

Extracellular vesicles (EVs) are continually released from cancer cells into biofluids, carrying actionable molecular fingerprints of the underlying disease with considerable diagnostic and therapeutic potential. The scarcity, heterogeneity and intrinsic complexity of tumor EVs present a major technological challenge in real-time monitoring of complex cancers such as glioblastoma (GBM). Surface-enhanced Raman spectroscopy (SERS) outputs a label-free spectroscopic fingerprint for EV molecular profiling. However, it has not been exploited to detect known biomarkers at the single EV level. We developed a multiplex fluidic device with embedded arrayed nanocavity microchips (MoSERS microchip) that achieves 97% confinement of single EVs in a minute amount of fluid (<10 μL) and enables molecular profiling of single EVs with SERS. The nanocavity arrays combine two featuring characteristics: (1) An embedded MoS2 monolayer that enables label-free isolation and nanoconfinement of single EVs due to physical interaction (Coulomb and van der Waals) between the MoS2 edge sites and the lipid bilayer; and (2) A layered plasmonic cavity that enables sufficient electromagnetic field enhancement inside the cavities to obtain a single EV level signal resolution for stratifying the molecular alterations. We used the GBM paradigm to demonstrate the diagnostic potential of the SERS single EV molecular profiling approach. The MoSERS multiplexing fluidic achieves parallel signal acquisition of glioma molecular variants (EGFRvIII oncogenic mutation and MGMT expression) in GBM cells. The detection limit of 1.23% was found for stratifying these key molecular variants in the wild-type population. When interfaced with a convolutional neural network (CNN), MoSERS improved diagnostic accuracy (87%) with which GBM mutations were detected in 12 patient blood samples, on par with clinical pathology tests. Thus, MoSERS demonstrates the potential for molecular stratification of cancer patients using circulating EVs.

Extracellular vesicles (EVs) are shed from cancer cells into body fluids, enclosing molecular information about the underlying disease with the potential for being the target cancer biomarker in emerging diagnosis approaches such as liquid biopsy. Still, the study of EVs presents major challenges due to their heterogeneity, complexity, and scarcity. Recently, liquid biopsy platforms have allowed the study of tumor‐derived materials, holding great promise for early‐stage diagnosis and monitoring of cancer when interfaced with novel adaptations of optical readouts and advanced machine learning analysis. Here, recent advances in labeled and label‐free optical techniques such as fluorescence, plasmonic, and chromogenic‐based systems interfaced with nanostructured sensors like nanoparticles, nanoholes, and nanowires, and diverse machine learning analyses are reviewed. The adaptability of the different optical methods discussed is compared and insights are provided into prospective avenues for the translation of the technological approaches for cancer diagnosis. It is discussed that the inherent augmented properties of nanostructures enhance the sensitivity of the detection of EVs. It is concluded by reviewing recent integrations of nanostructured‐based optical readouts with diverse machine learning models as novel analysis ventures that can potentially increase the capability of the methods to the point of translation into diagnostic applications.

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