Soft Sensors and Actuators for Wearable Human-Machine Interfaces.

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Colorimetric Detection of HER2-Overexpressing-Cancer-Derived Exosomes in Mouse Urine Using Magnetic-Polydiacetylene Nanoparticles.

Breast cancer (BC) is a major global health problem, with ≈20-25% of patients overexpressing human epidermal growth factor receptor 2 (HER2), an aggressive marker, yet access to early detection and treatment varies across countries. A low-cost, equipment-free, and easy-to-use polydiacetylene (PDA)-based colorimetric sensor is developed for HER2-overexpressing cancer detection, designed for use in low- and middle-income countries (LMICs). PDA nanoparticles are first prepared through thin-film hydration. Subsequently, hydrophilic magnetic nanoparticles and HER2 antibodies are sequentially conjugated to them. The synthesized HER2-MPDA can be concentrated and separated by a magnetic field while inheriting the optical characteristics of PDA. The specific binding of HER2 antibody in HER2-MPDA to HER2 receptor in HER2-overexpressing exosomes causes a blue-to-red color change by altering the molecular structure of the PDA backbone. This colorimetric sensor can simultaneously separate and detect HER2-overexpressing exosomes. HER2-MPDA can detect HER2-overexpressing exosomes in the culture medium of HER2-overexpressing BC cells and in mouse urine samples from a HER2-overexpressing BC mouse model. It can selectively isolate and detect only HER2-overexpressing exosomes through magnetic separation, and its detection limit is found to be 8.5 × 108 particles mL-1. This colorimetric sensor can be used for point-of-care diagnosis of HER2-overexpressing BC in LMICs.

Label-Free and Colorimetric Detection of Influenza A Virus via Receptor-Mediated Viral Fusion with Plasmonic Vesicles.

The rapid transmission and numerous re-emerging human influenza virus variants that spread via the respiratory system have led to severe global damage, emphasizing the need for detection tools that can recognize active and intact virions with infectivity. Here, this work presents a plasmonic vesicle-mediated fusogenic immunoassay (PVFIA) comprising gold nanoparticle (GNP) encapsulating fusogenic polymeric vesicles (plasmonic vesicles; PVs) for the label-free and colorimetric detection of influenza A virus (IAV). The PVFIA combines two sequential assays: a biochip-based immunoassay for target-specific capture and a PV-induced fusion assay for color change upon the IAV-PV fusion complex formation. The PVFIA demonstrates excellent specificity in capturing the target IAV, while the fusion conditions and GNP induce a significant color change, enabling visual detection. The integration of two consecutive assays results in a low detection limit (100.7919 EID50 mL-1 ) and good reliability (0.9901), indicating sensitivity that is 104.208 times higher than conventional immunoassay. Leveraging the PV viral membrane fusion activity renders the PVFIA promising for point-of-care diagnostics through colorimetric detection. The innovative approach addresses the critical need for detecting active and intact virions with infectivity, providing a valuable tool with which to combat the spread of the virus.

Weight change in patients with new-onset type 2 diabetes mellitus and its association with remission: Comprehensive real-world data.

AIMS: We aimed to investigate weight change in patients with new-onset type 2 diabetes mellitus and the association of weight loss on diabetes remission in Korean adults. MATERIALS AND METHODS: We used the health examination database of the Korean National Health Insurance Service. Patients diagnosed with type 2 diabetes mellitus from 2009 to 2012 were enrolled and followed to 2017. The baseline body weight was measured at the health examination closest to the time the patient was enrolled, and the change was calculated by examining the weight measured at the subsequent examination within 2 years. Remission was defined as fasting blood glucose less than 126 mg/dl at two or more consecutive health examinations after stopping medication. RESULTS: In total, 114, 874 patients with new-onset type 2 diabetes mellitus were analysed. Of these, 23 156 (20.2%) lost more than 5% of their body weight, and 2429 (2.1%) achieved remission. The adjusted odds ratio for remission in the weight loss group was 2.56 (95% confidence interval 2.35-2.79) compared with the group with stable body weight. Sensitivity analysis according to the degree of weight change showed that the greater weight loss, the higher the likelihood of remission. In the subgroup analysis, the effects of weight loss on remission were significantly greater in subgroups of age <65 years, male sex and body mass index >25. CONCLUSION: Weight loss within the first 2 years of treating type 2 diabetes mellitus was associated with diabetes remission. Physicians should pay more attention to weight management in new-onset type 2 diabetes mellitus, particularly for young and obese individuals.

Metabolic Syndrome, Modifiable Lifestyle Factors, and Sleep-Disordered Breathing: The Hispanic Community Health Study.

BACKGROUND: US Hispanics/Latinos are disproportionately susceptible to metabolic syndrome (MetS), attributed in part to systemic inequities related to health and lifestyle factors such as low physical activity (PA) levels, diet quality, alcohol use, tobacco use, and sleep disorder. Gender and heritage group differences are vastly understudied and need to be examined in this heterogeneous population. PURPOSE: To examine the relationships between select health and lifestyle factors and MetS among Hispanic gender and heritage subgroups (Hypothesis 1) and determine whether gender and heritage moderate those relationships (Hypothesis 2). METHODS: Participants included 14,155 Hispanic Americans aged 18-76 (59% female, mean age 45.92 ± 13.97) from seven heritage subgroups. This secondary analysis of cross-sectional data from the observational Hispanic Community Health Study/Study of Latinos (HCHS/SOL) dataset used hierarchical multinomial logistic regression to test Hypothesis 1; the dependent variable, MetS, included three categories delineating absence of MetS and presence of MetS with or without related medication use. Hayes' PROCESS macro tested Hypothesis 2. RESULTS: Low PA and sleep-disordered breathing (SDB) each had significant (p < .001) predictive value of MetS group membership, whereas both low and high alcohol use (p < .001) were associated with decreased MetS risk. Cigarette pack-years were not significantly associated with MetS outcomes. Gender moderated the association between MetS and alcohol use (p < .001), cigarette pack-years (p < .001), and SDB (p < .001) such that the effects on MetS were higher in females than males. The association between MetS and diet quality (p < .001) was stronger among males than in females. CONCLUSIONS: Gender and heritage differences were prominent among study variables.

Resistive gas sensors for the detection of NH3gas based on 2D WS2, WSe2, MoS2, and MoSe2: a review.

Transition metal dichalcogenides (TMDs) with a two-dimensional (2D) structure and semiconducting features are highly favorable for the production of NH3gas sensors. Among the TMD family, WS2, WSe2, MoS2, and MoSe2exhibit high conductivity and a high surface area, along with high availability, reasons for which they are favored in gas-sensing studies. In this review, we have discussed the structure, synthesis, and NH3sensing characteristics of pristine, decorated, doped, and composite-based WS2, WSe2, MoS2, and MoSe2gas sensors. Both experimental and theoretical studies are considered. Furthermore, both room temperature and higher temperature gas sensors are discussed. We also emphasized the gas-sensing mechanism. Thus, this review provides a reference for researchers working in the field of 2D TMD gas sensors.

Transcriptional regulation of genetic variants in the SLC40A1 promoter.

Solute carrier 40A1 (SLC40A1) encodes ferroportin, which is the only known transmembrane protein that exports elemental iron from mammalian cells and is essential for iron homeostasis. Mutations in SLC40A1 are associated with iron-overload disorders. In addition to ferroportin diseases, SLC40A1 expression is downregulated in various cancer types. Despite the clinical significance of the SLC40A1 transporter, only a few studies have investigated genetic variants in SLC40A1. The present study was performed to identify genetic variations in the SLC40A1 promoter and functionally characterize each variant using in vitro assays. We investigated four haplotypes and five variants in the SLC40A1 promoter. We observed that haplotype 3 (H3) had significantly lower promoter activity than H1, whereas the activity of H4 was significantly higher than that of H1. Luciferase activity of H2 was comparable to that of H1. In addition, four variants of SLC40A1, c.-1355G>C, c.-662C>T, c.-98G>C, and c.-8C>G, showed significantly increased luciferase activity compared to the wild type (WT), whereas c.-750G>A showed significantly decreased luciferase activity compared to the WT. Three transcription factors, cAMP response element-binding protein-1 (CREB-1), chicken ovalbumin upstream promoter transcription factor 1, and hepatic leukemia factor (HLF), were predicted to bind to the promoter regions of SLC40A1 near c.-662C>T, c.-98G>C, and c.-8C>G, respectively. Among these, CREB-1 and HLF bound more strongly to the variant sequences than to the WT and functioned as activators of SLC40A1 transcription. Collectively, our findings indicate that the two SLC40A1 promoter haplotypes affect SLC40A1 transcription, which is regulated by CREB-1 and HLF.

Age-Dependent Serum Volatilomics of Milk and Yogurt Intake: A Randomized Crossover Study in Healthy Young and Older Men.

Nutritional biomarkers of dairy intake can be affected by both food transformation and the metabolic status of the consumer. To assess these effects, this study investigated the serum volatilome of 14 young (YA) and 14 older (OA) adult men undergoing a 3 week restriction of dairy and fermented foods followed by a randomized crossover acute intake of milk and yogurt. 3,5-Dimethyl-octan-2-one was identified as a potential marker of dairy product intake as its response after both milk and yogurt intake was significantly increased during the postprandial phase but significantly decreased in fasting serum samples of the OA group after the restriction phase. The postprandial response of two metabolites was significantly different for the two dairy products while 19 metabolites were modulated by age. Remarkably, the response of all age-dependent metabolites was higher in the OA than in the YA group after milk or yogurt intake, whereas at the end of the restriction phase, their fasting concentrations were lower in the OA than in the YA group. Among these, p-cresol, a specific marker of colonic protein fermentation, had a significant response in the OA but not the YA group, which may suggest impaired intestinal processing of dietary proteins in the OA group.

A Microfluidic System for Investigating Anticipatory Medication Effects on Dopamine Homeostasis in Dopaminergic Cells.

Dopamine (DA) homeostasis influences emotions, neural circuit development, cognition, and the reward system. Dysfunctions in DA regulation can lead to neurological disorders, including depression, developmental disorders, and addiction. DA homeostasis disruption is a primary cause of Parkinson's Disease (PD). Therefore, understanding the relationship between DA homeostasis and PD progression may clarify the mechanisms for pharmacologically treating PD. This study developed a novel in vitro DA homeostasis platform which consists of three main parts: (1) a microfluidic device for culturing DAergic neurons, (2) an optical detection system for reading DA levels, and (3) an automatic closed-loop control system that establishes when and how much medication to infuse; this uses a microfluidic device that can cultivate DAergic neurons, perfuse solutions, perform in vitro PD modeling, and continuously monitor DA concentrations. The automatically controlled closed-loop control system simultaneously monitors pharmacological PD treatment to support long-term monitoring of DA homeostasis. SH-SY5Y neuroblastoma cells were chosen as DAergic neurons. They were cultivated in the microfluidic device, and real-time cellular DA level measurements successfully achieved long-term monitoring and modulation of DA homeostasis. When applied in combination with multiday cell culture, this advanced system can be used for drug screening and fundamental biological studies.

Design and Synthesis of CdHgSe/HgS/CdZnS Core/Multi-Shell Quantum Dots Exhibiting High-Quantum-Yield Tissue-Penetrating Shortwave Infrared Luminescence.

Cdx Hg1- x Se/HgS/Cdy Zn1- y S core/multi-shell quantum dots (QDs) exhibiting bright tissue-penetrating shortwave infrared (SWIR; 1000-1700 nm) photoluminescence (PL) are engineered. The new structure consists of a quasi-type-II Cdx Hg1- x Se/HgS core/inner shell domain creating luminescent bandgap tunable across SWIR window and a wide-bandgap Cdy Zn1- y S outer shell boosting the PL quantum yield (QY). This compositional sequence also facilitates uniform and coherent shell growth by minimizing interfacial lattice mismatches, resulting in high QYs in both organic (40-80%) and aqueous (20-70%) solvents with maximum QYs of 87 and 73%, respectively, which are comparable to those of brightest visible-to-near infrared QDs. Moreover, they maintain bright PL in a photocurable resin (QY 40%, peak wavelength ≈ 1300 nm), enabling the fabrication of SWIR-luminescent composites of diverse morphology and concentration. These composites are used to localize controlled amounts of SWIR QDs inside artificial (Intralipid) and porcine tissues and quantitatively evaluate the applicability as luminescent probes for deep-tissue imaging.

Bone mineral density and lipid profiles in older adults: a nationwide cross-sectional study.

It has been hypothesized that lipid profiles are associated with bone mineral density (BMD), but previous results have been controversial. In this study, serum triglycerides showed a significant inverse association with BMD, and the relationship is thought to correlate with vitamin D status among older adults. INTRODUCTION: The purpose of this study was to investigate the relationship between lipid profiles and bone mineral density (BMD) in older adults using data from the Korean National Health and Nutrition Examination Survey (KNHANES). METHODS: We enrolled men older than 50 years and postmenopausal women who participated in the KNHANES 2008-2011. Subjects with liver cirrhosis, thyroid disease, or renal dysfunction and those receiving treatment for hyperlipidemia or osteoporosis were excluded. RESULTS: A total of 4323 subjects (2286 men and 2037 women) was analyzed. The prevalence of osteoporosis was 8.7% in men older than 50 years and 38.4% in postmenopausal women. Osteopenia and osteoporosis groups were generally older and tended to have a lower body mass index compared to the normal group (p for trend < 0.001). The correlation between each lipid profile and BMD was analyzed in the linear model adjusted for age and body mass index. Total cholesterol and high-density lipoprotein cholesterol showed a negative correlation with BMD in the total population, but there was no significant correlation when analyzed separately for men and women. Triglycerides had a negative association with whole-body BMD in both men and women (p < 0.05). The adjusted odds ratio of logarithmic triglyceride level for osteoporosis was 2.50 (95% confidence interval 1.13-5.51) in women older than 65 years. CONCLUSION: Serum triglycerides showed a significant inverse association with BMD, and the relationship is thought to correlate with vitamin D status among older adults.

Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer's disease using a high-performance interpretable deep learning network.

OBJECTIVES: To develop and validate an automatic classification algorithm for diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI). METHODS AND MATERIALS: This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning-based AD signature areas were investigated. RESULTS: Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947-0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951-0.955], p = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex. CONCLUSIONS: TabNet shows high performance in AD classification and detailed interpretation of the selected regions. CLINICAL RELEVANCE STATEMENT: Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer's disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions. KEY POINTS: • MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer's disease (AD) from cognitive normal cases, which was comparable with that of XGBoost. • The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet. • Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

Vision for tobacco endgame in Korea: suggestions for countries with endgame aspirations.

Tobacco endgame is a focal point of discussion at both national and international levels. We aimed to describe efforts related to achieving the tobacco endgame in the Republic of Korea, an exemplar of a country with endgame aspirations, and compare them with the efforts of other nations. We reviewed the tobacco endgame efforts of three nations considered tobacco control leaders: New Zealand (NZ), Australia and Finland. The efforts/attempts of each country were described using an endgame strategy category. The tobacco control leaders had explicit goals to achieve a smoking prevalence of <5% before a target date and had legislation and research centres for tobacco control and/or endgame. NZ is implementing a mixture of conventional and innovative endgame interventions; the others use incremental conventional approaches. In Korea, there has been an attempt to ban the sale and manufacture of combustible cigarettes. The attempt led to the filing of a petition, and a survey of adults showed 70% supported the legislation banning tobacco. The Korean government mentioned a tobacco endgame in a 2019 plan, yet a target and an end date were absent. The 2019 plan in Korea included incremental FCTC strategies. Practices in the leading countries show that legislation and research are key to ending the tobacco epidemic. The MPOWER measures must be strengthened, endgame objectives must be set and bold strategies must be adopted. Key endgame policies include those with evidence of effectiveness, such as retailer reductions.

Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives.

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.

Multi-Camera-Based Sorting System for Surface Defects of Apples.

In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, potentially leading to misclassification due to defects in unscanned areas. Various methods were proposed where apples were rotated using rollers on a conveyor. However, since the rotation was highly random, it was difficult to scan the apples uniformly for accurate classification. To overcome these limitations, we proposed a multi-camera-based apple sorting system with a rotation mechanism that ensured uniform and accurate surface imaging. The proposed system applied a rotation mechanism to individual apples while simultaneously utilizing three cameras to capture the entire surface of the apples. This method offered the advantage of quickly and uniformly acquiring the entire surface compared to single-camera and random rotation conveyor setups. The images captured by the system were analyzed using a CNN classifier deployed on embedded hardware. To maintain excellent CNN classifier performance while reducing its size and inference time, we employed knowledge distillation techniques. The CNN classifier demonstrated an inference speed of 0.069 s and an accuracy of 93.83% based on 300 apple samples. The integrated system, which included the proposed rotation mechanism and multi-camera setup, took a total of 2.84 s to sort one apple. Our proposed system provided an efficient and precise solution for detecting defects on the entire surface of apples, improving the sorting process with high reliability.

Detection and Length Measurement of Cracks Captured in Low Definitions Using Convolutional Neural Networks.

Continuous efforts were made in detecting cracks in images. Varied CNN models were developed and tested for detecting or segmenting crack regions. However, most datasets used in previous works contained clearly distinctive crack images. No previous methods were validated on blurry cracks captured in low definitions. Therefore, this paper presented a framework of detecting the regions of blurred, indistinct concrete cracks. The framework divides an image into small square patches which are classified into crack or non-crack. Well-known CNN models were employed for the classification and compared with each other with experimental tests. This paper also elaborated on critical factors-the patch size and the way of labeling patches-which had considerable influences on the training performance. Furthermore, a series of post-processes for measuring crack lengths were introduced. The proposed framework was tested on the images of bridge decks containing blurred thin cracks and showed reliable performance comparable to practitioners.

Room Temperature Chemiresistive Gas Sensors Based on 2D MXenes.

Owing to their large surface area, two-dimensional (2D) semiconducting nanomaterials have been extensively studied for gas-sensing applications in recent years. In particular, the possibility of operating at room temperature (RT) is desirable for 2D gas sensors because it significantly reduces the power consumption of the sensing device. Furthermore, RT gas sensors are among the first choices for the development of flexible and wearable devices. In this review, we focus on the 2D MXenes used for the realization of RT gas sensors. Hence, pristine, doped, decorated, and composites of MXenes with other semiconductors for gas sensing are discussed. Two-dimensional MXene nanomaterials are discussed, with greater emphasis on the sensing mechanism. MXenes with the ability to work at RT have great potential for practical applications such as flexible and/or wearable gas sensors.

Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders.

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in ß and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

A Biodegradable Magnetic Microrobot Based on Gelatin Methacrylate for Precise Delivery of Stem Cells with Mass Production Capability.

A great deal of research has focused on small-scale robots for biomedical applications and minimally invasive delivery of therapeutics (e.g., cells, drugs, and genes) to a target area. Conventional fabrication methods, such as two-photon polymerization, can be used to build sophisticated micro- and nanorobots, but the long fabrication cycle for a single microrobot has limited its practical use. This study proposes a biodegradable spherical gelatin methacrylate (GelMA) microrobot for mass production in a microfluidic channel. The proposed microrobot is fabricated in a flow-focusing droplet generator by shearing a mixture of GelMA, photoinitiator, and superparamagnetic iron oxide nanoparticles (SPIONs) with a mixture of oil and surfactant. Human nasal turbinate stem cells (hNTSCs) are loaded on the GelMA microrobot, and the hNTSC-loaded microrobot shows precise rolling motion in response to an external rotating magnetic field. The microrobot is enzymatically degraded by collagenase, and released hNTSCs are proliferated and differentiated into neuronal cells. In addition, the feasibility of the GelMA microrobot as a cell therapeutic delivery system is investigated by measuring electrophysiological activity on a multielectrode array. Such a versatile and fully biodegradable microrobot has the potential for targeted stem cell delivery, proliferation, and differentiation for stem cell-based therapy.

A flap endonuclease 1-assisted universal viral nucleic acid sensing system using surface-enhanced Raman scattering.

The continued uncertainty of emerging infectious viral diseases has led to an extraordinary urgency to develop advanced molecular diagnostic tests that are faster, more reliable, simpler to use, and readily available than traditional methods. This study presents a system that can accurately and rapidly trace viral nucleic acids by employing flap endonuclease 1 (FEN1)-assisted specific DNA cleavage reactions and surface-enhanced Raman scattering (SERS)-based analysis. The designed Raman tag-labeled 5'- and 3'-flap provider DNA yielded structurally defined DNA substrates on magnetic nanoparticle surfaces when a target was present. The FEN1 enzyme subsequently processes the substrates formed via an invasive cleavage reaction, producing 5'-flap DNA products. Magnetic separation allows efficient purification of flap products from reaction mixtures. The isolated solution was directly applied onto high aspect-ratio plasmonic silver nanopillars serving as SERS-active substrates to induce amplified SERS signals. We verified the developed SERS-based sensing system using a synthetic target complementary to an avian influenza A (H9N2) virus gene and examined the detection performance of the system using complementary DNA (cDNA) derived from H9N2 viral RNA. As a result, we could detect a synthetic target with a detection limit of 41.1 fM with a single base-pair discrimination ability and achieved multiplexed detection capability for two targets. Using cDNA samples from H9N2 viruses, we observed a high concordance of R2 = 0.917 between the data obtained from SERS and the quantitative polymerase chain reaction. We anticipate that this enzyme-assisted SERS sensor may provide insights into the development of high-performance molecular diagnostic tools that can respond rapidly to viral pathogens.