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The differential diagnosis between mild cognitive impairment (MCI) and Alzheimer's disease (AD) has been highly demanded for its effectiveness in preventing and contributing to early diagnosis of AD. To this end, we developed a single plasmonic asymmetric nanobridge (PAN)-based biosensor to differentially diagnose MCI and AD by quantitative profiling of phosphorylated tau proteins (p-tau) in clinical plasma samples, which revealed a significant correlation with AD development and progression. The PAN was designed to have a conductive junction and asymmetric structure, which was unable to be synthesized by the traditional thermodynamical methods. For its unique morphological characteristics, PAN features high electromagnetic field enhancement, enabling the biosensor to achieve high sensitivity, with a limit of detection in the attomolar regime for quantitative analysis of p-tau. By introducing support vector machine (SVM)-based machine learning algorithm, the improved diagnostic system was achieved for prediction of healthy controls, MCI, and AD groups with an accuracy of 94.47 % by detecting various p-tau species levels in human plasma. Thus, our proposed PAN-based plasmonic biosensor has a powerful potential in clinical utility for predicting the onset of AD progression in the asymptomatic phase., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The outbreak of emerging infectious diseases gave rise to the demand for reliable point-of-care testing methods to diagnose and manage those diseases in early onset. However, the current on-site testing methods including lateral flow immunoassay (LFIA) suffer from the inaccurate diagnostic result due to the low sensitivity. Herein, we present the surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-LFIA) by introducing phage-templated hierarchical plasmonic assembly (PHPA) nanoprobes to diagnose a contagious disease. The PHPA was fabricated using gold nanoparticles (AuNPs) assembled on bacteriophage MS2, where inter-particle gap sizes can be adjusted by pH-induced morphological alteration of MS2 coat proteins to provide the maximum SERS amplification efficiency via plasmon coupling. The plasmonic probes based on the PHPA produce strong and reproducible SERS signal that leads to sensitive and reliable diagnostic results in SERS-LFIA. The developed SERS-LFIA targeting severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) antibodies for a proof of concept had <100 pg/mL detection limits with high specificity in serum, proving it as an effective diagnostic device for the infectious diseases. Clinical validation using human serum samples further confirmed that the PHPA-based SERS-LFIA can distinguish the patients with COVID-19 from healthy controls with significant accuracy. These outcomes prove that the developed SERS-LFIA biosensor can be an alternative point-of-care testing (POCT) method against the emerging infectious diseases, in combination with the commercially available portable Raman devices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Alzheimer's disease (AD) is a neurodegenerative disease of complex pathogenesis, with overt symptoms following disease progression. Early AD diagnosis is challenging due to the lack of robust biomarkers and limited patient access to diagnostics via neuroimaging and cerebrospinal fluid (CSF) tests. Exosomes present in body fluids are attracting attention as diagnostic biomarkers that directly reflect neuropathological features within the brain. In particular, exosomal miRNAs (exomiRs) signatures are involved in AD pathogenesis, showing a different expression between patients and the healthy controls (HCs). However, low yield and high homologous nature impede the accuracy and reproducibility of exosome blood-based AD diagnostics. Here, we developed a programmable curved plasmonic nanoarchitecture-based biosensor to analyze exomiRs in clinical serum samples for accurate AD diagnosis. To allow the detection of exomiRs in serum at attomolar levels, nanospaces (e.g., nanocrevice and nanocavity) were introduced into the nanostructures to dramatically increase the spectral sensitivity by adjusting the bending angle of the plasmonic nanostructure through sodium chloride concentration control. The developed biosensor classifies individuals into AD, mild cognitive impairment (MCI) patients, and HCs through profiling and quantifying exomiRs. Furthermore, integrating analysis expression patterns of multiple exosomal biomarkers improved serum-based diagnostic performance (average accuracy of 98.22%). Therefore, precise, highly sensitive serum-derived exosomal biomarker detection-based plasmonic biosensor has a robust capacity to predict the molecular pathologic of neurodegenerative disease, progression of cognitive decline, MCI/AD conversion, as well as early diagnosis and treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The urinary exosomal miRNAs are recently emerging prostate cancer (PC)-associated biomarkers for the early-stage diagnosis and prognosis due to their non-invasiveness, inherent stability and the representation of the status of the originated cells. However, developing a urinary exosomal miRNA detection method with high accuracy is challenging because of the low abundance and high sequence homology of miRNAs. Herein, we present a quantitative and label-free miRNA sensing platform using surface-enhanced Raman scattering (SERS) based on three-dimensional (3D) hierarchical plasmonic nano-architecture to detect urinary exosomal miRNAs. This hierarchical nanostructure is constructed by self-assembly between target-complementary DNA probes-conjugated gold nanoparticles and head-flocked gold nanopillars in the presence of the target miRNAs, creating numerous 3D plasmonic hot-spots inducing exceedingly high amplification of SERS signals. This 3D SERS biosensor achieved ∼10 aM detection limits for the target miRNAs (miR-10a and miR-21), which is over 1000-fold more sensitive than previously reported miRNA sensors without the requirement of any labelling or pre-treatment steps. Finally, the clinical validation using urinary samples revealed that our 3D SERS sensor discriminates PC patients from healthy control with high diagnostic accuracy (0.93) based on the differential expression level of urinary exosomal miRNAs. These outputs demonstrate that our SERS sensor based on 3D hierarchical nano-architecture can offer facile, accurate and rapid methods to measure miRNA expression and is helpful for the diagnosis of various diseases., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Alzheimer's disease (AD), the most common neurologic disorder, is characterized by progressive cognitive impairment. However, the low clinical significance of the currently used core AD biomarkers amyloid-beta and tau proteins remains a challenge. Recently, exosomes, found in human biological fluids, are gaining increasing attention because of their clinical significance in diagnosing of various diseases. In particular, blood-derived exosomal miRNAs are not only stable but also provide information regarding the different characteristics according to AD progression. However, quantitative and qualitative detection is difficult due to their characteristics, such as small size, low abundance, and high homology. Here, we present a DNA-assembled advanced plasmonic architecture (DAPA)-based plasmonic biosensor to accurately detect exosomal miRNAs in human serum. The designed nanoarchitecture possesses two narrow nanogaps that induce plasmon coupling; this significantly enhances its optical energy density, resulting in a 1.66-fold higher refractive-index (RI) sensitivity than nanorods at localized surface plasmon resonance (LSPR). Thus, the proposed biosensor is ultrasensitive and capable of selective single-nucleotide detection of exosomal miRNAs at the attomolar level. Furthermore, it identified AD patients from healthy controls by measuring the levels of exosomal miRNA-125b, miRNA-15a, and miRNA-361 in clinical serum samples. In particular, the combination of exosomal miRNA-125b and miRNA-361 showed the best diagnostic performance with a sensitivity of 91.67%, selectivity of 95.00%, and accuracy of 99.52%. These results demonstrate that our sensor can be clinically applied for AD diagnosis and has great potential to revolutionize the field of dementia research and treatment in the future., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Blood-based detection of Alzheimer's disease (AD) biomarker has become a prominent method for diagnosis of AD which can replace the complex and invasive cerebrospinal fluid (CSF)-based diagnostic method. However, the application of blood AD biomarker in actual AD diagnosis is hampered by the extremely low concentration of biomarkers in blood, as well as the existence of interfering proteins. Therefore, it is essential to develop a sensitive and specific detection platform to achieve blood-based diagnosis of AD. Here, a surface-enhanced Raman scattering (SERS)-based sensor is developed for the quantitative determination of tau protein in the plasma of AD patients. To acquire femtomolar-level detection limit, this platform involves the use of half antibody fragment immobilized onto head-flocked gold nanopillar SERS substrates and SERS-nanotags. The small size of the half antibody fragment maximizes the effect of plasmon coupling, by reducing the distance between SERS substrates and SERS-nanotags. Also, the use of half antibody fragment improves the antigen recognition ability by immobilizing the antibody with high density and efficient orientation of the antibody. The sensor using these characteristics showed a low detection limit of 3.21 fM and a wide detection range (10 fM - 1 μM). The platform was also able to accurately quantify the tau protein in the clinical plasma sample and correctly distinguish the AD patient from the healthy control. The ultrasensitive and specific SERS immunoassay platform facilitates accurate and early detection of AD biomarkers and can serve as a valuable tool for simple point-of-care testing in clinical diagnosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Cell-free micro-ribonucleic acids (cf-miRNAs) regulate most human genes by controlling the mRNA translation and have been considered as the most promising biomarkers for cancer diagnosis and prognosis. Due to their low abundance, short length, and similar sequences, an attractive approach that is ultrasensitive and label-free is highly demanded. Herein, we develop a label-free, ultra-high sensitivity and selectivity multiplex miRNA using surface-enhanced Raman scattering to detect cancer-associated miRNAs with head-flocked gold nanopillars as a substrate. Using a complementary DNA probe platform and an ultra-high sensitivity SERS based biosensor, we achieved high selectivity in targeting miRNAs with extremely low detection limits, without any chemical or enzymatic reactions and evaluated the correlation between the miRNA expression level and the Raman signal intensity. A reproducible SERS signal is monitored via uniformly fabricated elastic head-flocked gold nanopillars through a mapping method. This system supports the detection of cell-free miRNAs in blood, which are utilized and quantified as biomarkers to diagnose and provide a prognosis for cancers and intractable diseases. In the foreseeable future, this advanced label-free miRNA detection system is highly promising for a less tumor-invasive biopsy for the early diagnosis and prognosis of cancer.

This paper describes a signal enhancement strategy to improve the sensitivity of an antibody-based immunosensor that uses polydiacetylene (PDA) liposomes to detect a target protein (human immunoglobulin E [hIgE]). To achieve ultrasensitive detection, multiple stimuli applied to PDA immunosensor chips offer a signal enhancement method that combines the primary immune reaction between antigen and antibody with the sandwich method of polyclonal antibody (pAb)-conjugated horseradish peroxidase (HRP). In the second step, fluorescence is enhanced by the mechanical pressure from the precipitate formed by enzyme catalysis. In order to detect hIgE, the surface of immobilized PDA liposomes was conjugated with monoclonal antibodies against hIgE, and fluorescence signals were detected after the antigen-antibody reaction. In this step, hIgE concentrations as low as 10 ng/mL were detected. Fluorescence signals slightly increased when anti-hIgE pAb-HRP was used as an amplifying agent after primary immunoresponse. After secondary immunoresponse, HRP-catalyzed oxidation of 3,3'-diaminobenzidine produced an insoluble precipitate that strongly stimulated PDA liposomes by their weight and pressure, thereby dramatically increasing the fluorescence signal. Thus, PDA liposome immunosensor could detect hIgE concentrations as low as 0.01 ng/mL, representing a 1000-fold increase in sensitivity over the signal generated by the primary immunoresponse. This study indicates that increasing the external mechanical force applied to PDA liposomes by enzyme-catalyzed precipitate formation enhanced the sensitivity of the PDA liposome immunosensor chip. This strategy can be applied to the detection of other biomolecules in experimental or clinical settings where ultrasensitive and highly specific biosensing is required., (Copyright © 2014 Elsevier B.V. All rights reserved.)