Conventional Detection Methods Research Articles

Dual-drive strategy-based modulation of the interfacial charge of S-scheme heterojunction and photoelectrochemical ultrasensitive detection of CD44

The efficacy of charge carrier separation plays a crucial role in influencing the practical application of photoelectrochemical (PEC) detection platforms, which can be meticulously engineered to realize highly sensitive detection at the sensing interface. Hence, a novel dual-drive strategy was proposed to integrate thermally-assisted external drive behavior with the internal drive behavior of sulfur vacancies (SVs), realizing the rapid separation of charge at the S-scheme heterojunction sensing interface. On the one hand, unabsorbed solar energy was utilized an external driving force to facilitate efficient photothermoelectric conversion within the In2S3/CdS heterojunction, achieving the photocurrent response intensity 1.5 times higher than conventional PEC detection methods. On the other hand, introducing sulfur vacancies (SVs) as internal driving forces induced ordered migration and directional flow of charge carriers. Moreover, density functional theory (DFT) calculations and experimental results demonstrated that photo-generated carriers achieved rapid separation along the S-scheme heterojunction. Under the optimal conditions, a biosensor constructed based on the dual-drive strategy exhibited high sensitivity for the CD44 (cluster of differentiation-44) cluster, with a low detection limit of 0.132 pg mL−1and a wide linear range of 0.001 ∼ 1000 ng mL−1. The proposed strategy offers new insights into how to effectively harness excess energy in the traditional ultraviolet spectral range for ultrasensitive biosensors.

Disposable electrochemical biosensors for the detection of bacteria in the light of antimicrobial resistance.

Persistent and inappropriate use of antibiotics is causing rife antimicrobial resistance (AMR) worldwide. Common bacterial infections are thus becoming increasingly difficult to treat without the use of last resort antibiotics. This has necessitated a situation where it is imperative to confirm the infection to be bacterial, before treating it with antimicrobial speculatively. Conventional methods of bacteria detection are either culture based which take anywhere between 24 and 96 hor require sophisticated molecular analysis equipment with libraries and trained operators. These are difficult propositions for resource limited community healthcare setups of developing or less developed countries. Customized, inexpensive, point-of-care (PoC) biosensors are thus being researched and developed for rapid detection of bacterial pathogens. The development and optimization of disposable sensor substrates is the first and crucial step in development of such PoC systems. The substrates should facilitate easy charge transfer, a high surface to volume ratio, be tailorable by the various bio-conjugation chemistries, preserve the integrity of the biorecognition element, yet be inexpensive. Such sensor substrates thus need to be thoroughly investigated. Further, if such systems were made disposable, they would attain immunity to biofouling. This article discusses a few potential disposable electrochemical sensor substrates deployed for detection of bacteria for environmental and healthcare applications. The technologies have significant potential in helping reduce bacterial infections and checking AMR. This could help save lives of people succumbing to bacterial infections, as well as improve the overall quality of lives of people in low- and middle-income countries.

Transfer Learning-Based Structural Damage Identification for Building Structures with Limited Measurement Data

Structural damage detection is crucial for ensuring the safety of civil building structures in operational environments. Recently, deep learning-based methods have gained increasing attention from engineers and researchers. The performance of conventional deep learning methods for structural damage detection relies on a large number of labeled training datasets. However, it is difficult or/and impossible to obtain sufficient datasets to cover various damage scenarios for in-service structures. A little research has been conducted to identify both the damage severity and location with limited labeled measurement data. A novel transfer learning-based method for structural damage identification with limited measurements has been proposed utilizing frequency response functions (FRFs) as the input. The real structure is regarded as the target domain and its numerical model is as the source domain. The samples for various damage scenarios are generated using the numerical model, and a designed deep convolutional neural network (CNN) is pre-trained. The knowledge of the pre-trained network is transferred to identify the damage location and severity of the real structure using limited measurement data. Numerical and experimental studies have been conducted on a three-story building structure to verify the performance of the proposed method. To understand transfer learning and model interpretability, the t-SNE feature visualization is adopted to show the feature distribution changes during transfer learning. Numerical and experimental results show that the proposed approach outperforms conventional CNN models, and it is effective and accurate in identifying structural damage location and severity in real structures with limited measurement data.

Fall Detection System for Elderly Care

Abstract: This project introduces a revolutionary Fall Detection System for Elderly Care, focusing on swift detection and immediate response to falls, particularly among wheelchair-bound seniors. In response to the escalating challenges posed by an aging population, Especially concerning the well-being of the elderly, the development of innovative solutions has become imperative. Central to these concerns is the prevalence of falls among seniors, which not only jeopardizes their physical health but also threatens their independence. Conventional fall detection methods have proven inadequate in providing timely assistance, underscoring the critical need for more advanced technologies. This project addresses this pressing need through the creation of a comprehensive Fall Detection System, with a specific focus on wheelchair-bound individuals. By seamlessly integrating state-of-the-art hardware and software components, the system aims to revolutionize elderly care by prioritizing safety and autonomy. At its core, the system utilizes the MPU6050 sensor module, renowned for its precision in monitoring body position and movement. Through the implementation of sophisticated algorithms, the software component meticulously analyzes real-time sensor data to swiftly detect falls. Upon detection of a fall event, the system autonomously triggers alerts to nearby healthcare services or designated contacts, ensuring a rapid response and timely provision of assistance to the elderly individual in need.

Sparse Wasserstein stationary subspace analysis for fault detection and diagnosis of nonstationary industrial processes

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ2,p-norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster.

Advancements in Chemical and Biosensors for Point-of-Care Detection of Acrylamide.

Acrylamide (AA), an odorless and colorless organic small-molecule compound found generally in thermally processed foods, possesses potential carcinogenic, neurotoxic, reproductive, and developmental toxicity. Compared with conventional methods for AA detection, bio/chemical sensors have attracted much interest in recent years owing to their reliability, sensitivity, selectivity, convenience, and low cost. This paper provides a comprehensive review of bio/chemical sensors utilized for the detection of AA over the past decade. Specifically, the content is concluded and systematically organized from the perspective of the sensing mechanism, state of selectivity, linear range, detection limits, and robustness. Subsequently, an analysis of the strengths and limitations of diverse analytical technologies ensues, contributing to a thorough discussion about the potential developments in point-of-care (POC) for AA detection in thermally processed foods at the conclusion of this review.

DETECTION OF ANDROID BOTNETS

In the dynamic landscape of cybersecurity, the persistent proliferation of botnets remains a formidable challenge. Conventional detection methods often grapple with elevated false positive rates and struggle to keep pace with the evolving tactics employed by malicious actors. This study delves into the domain of botnet detection, with a primary focus on refining the identification process for HTTP-based botnets. Our approach harnesses the power of K-Nearest Neighbors (KNN) and Logistic Regression algorithms, strategically amalgamating their capabilities to navigate the intricate digital terrain. Departing from prior studies, we directly confront the botnet detection challenge by synergizing two robust machine learning techniques. Through the fusion of KNN and Logistic Regression, our system achieves unparalleled accuracy and efficiency in discerning HTTP botnet activities. Furthermore, this research pioneers a groundbreaking methodology for botnet detection, centering around the innovative fusion of TFIDF and Textrank algorithms. This hybrid approach significantly bolsters the precision of information extraction and summarization. In an age inundated with vast digital datasets, our method adeptly sifts through information while furnishing concise and relevant summaries, thereby conserving invaluable time and resources. The distinctive aspect of our approach lies in its ability to swiftly adapt to emerging botnet strategies. Through extensive testing and comparative analysis against existing models, our system surpasses prior methodologies, demonstrating its proficiency in accurately identifying botnet activities while minimizing false positives. Furthermore, our solution serves as an exemplar of efficiency, facilitating prompt and effective identification of pernicious botnets that imperil data security Key Words: Botnet Detection, Cybersecurity, K-Nearest Neighbors (KNN), Logistic Regression, TFIDF, Textrank, Information Summarization, Digital Security, Malware Detection.

Advances in Microfluidic Paper-Based Analytical Devices (µPADs): Design, Fabrication, and Applications.

Microfluidic Paper-based Analytical Devices (µPADs) have emerged as a new class of microfluidic systems, offering numerous advantages over traditional microfluidic chips. These advantages include simplicity, cost-effectiveness, stability, storability, disposability, and portability. As a result, various designs for different types of assays are developed and investigated. In recent years, µPADs are combined with conventional detection methods to enable rapid on-site detection, providing results comparable to expensive and sophisticated large-scale testing methods that require more time and skilled personnel. The application of µPAD techniques is extensive in environmental quality control/analysis, clinical diagnosis, and food safety testing, paving the way for on-site real-time diagnosis as a promising future development. This review focuses on the recent research advancements in the design, fabrication, material selection, and detection methods of µPADs. It provides a comprehensive understanding of their principles of operation, applications, and future development prospects.

An Allysine-Conjugatable Probe for Fluorogenically Imaging Fibrosis.

Allysine, a pivotal biomarker in fibrogenesis, has prompted the development of various radioactive imaging probes. However, fluorogenic probes targeting allysine remain largely unexplored. Herein, by leveraging the equilibrium between the nonfluorescent spirocyclic and the fluorescent zwitterionic forms of rhodamine-cyanine hybrid fluorophores, we systematically fine-tuned the environmental sensitivity of this equilibrium toward the development of fluorogenic probes for fibrosis. The trick lies in modulating the nucleophilicity of the ortho-carboxyl group, which is terminated with a hydrazide group for allysine conjugation. Probe B2 was developed with this strategy, which featured an N-sulfonyl amide group and exhibited superior fibrosis-to-control imaging contrast. Initially presenting as nonfluorescent spirocyclic aggregates in aqueous solutions, B2 displayed a notable fluorogenic response upon conjugation with protein allysine through its hydrazide group, inducing deaggregation and switching to the fluorescent zwitterionic form. Probe B2 outperformed the traditional Masson stain in imaging contrast, achieving an about 260-2600-fold ratio for fibrosis-to-control detection depending on fibrosis severity. Furthermore, it demonstrated efficacy in evaluating antifibrosis drugs. Our results emphasize the potential of this fluorogenic probe as an alternative to conventional fibrosis detection methods. It emerges as a valuable tool for antifibrosis drug evaluation.

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

Label-free white blood cells classification using a deep feature fusion neural network

White blood cell (WBC) classification is a valuable diagnostic approach for identifying diseases. However, conventional methods for WBC detection, such as flow cytometers, have limitations in terms of their high cost, large system size, and laborious staining procedures. As a result, deep learning-based label-free WBC image analysis methods are gaining popularity. Nevertheless, most existing deep learning WBC classification techniques fail to effectively utilize the subtle differences in the internal structures of WBCs observed under a microscope. To address this issue, we propose a neural network with feature fusion in this study, which enables the detection of label-free WBCs. Unlike conventional convolutional neural networks (CNNs), our approach combines low-level features extracted by shallow layers with high-level features extracted by deep layers, generating fused features for accurate bright-field WBC identification. Our method achieves an accuracy of 80.3 % on the testing set, demonstrating a potential solution for deep-learning-based biomedical diagnoses. Considering the proposed method simplifies the cell detection process and eliminates the need for complex operations like fluorescent staining, we anticipate that this automatic and label-free WBC classification network could facilitate more precise and effective analysis, and it could contribute to the future adoption of miniatured flow cytometers for point-of-care (POC) diagnostics applications.

Advancements in nanoparticle-based therapies for multidrug-resistant candidiasis infections: a comprehensive review.

Candida auris, a rapidly emerging multidrug-resistant fungal pathogen, poses a global health threat, with cases reported in over 47 countries. Conventional detection methods struggle, and the increasing resistance of C. auris to antifungal agents has limited treatment options. Nanoparticle-based therapies, utilizing materials like silver, carbon, zinc oxide, titanium dioxide, polymer, and gold, show promise in effectively treating cutaneous candidiasis. This review explores recent advancements in nanoparticle-based therapies, emphasizing their potential to revolutionize antifungal therapy, particularly in combating C. auris infections. The discussion delves into mechanisms of action, combinations of nanomaterials, and their application against multidrug-resistant fungal pathogens, offering exciting prospects for improved clinical outcomes and reduced mortality rates. The aim is to inspire further research, ushering in a new era in the fight against multidrug-resistant fungal infections, paving the way for more effective and targeted therapeutic interventions.

Mapping and predicting cassava mosaic disease outbreaks using earth observation and meteorological data-driven approaches

Cassava Mosaic Disease (CMD) poses a significant threat to cassava (Manihot esculenta Crantz) production, both globally and in Thailand. Caused by the Cassava Mosaic Begomovirus (CMB), CMD conventional detection methods involve field-based visual assessment and mapping, which can be time-consuming and labour-intensive.In this study, field-based surveys have been conducted during the early wet season cassava planting period in three different productive regions of Thailand. Five classification models (namely ANN, RF, XGBoost, SVM, and Ensemble learning) were employed to predict CMD occurrence with various sets of optimized predictive meteorological and remote sensing features. Field based CMD observations have been used for modelling validation and accuracy assessment.Utilizing time series of Sentinel-2 MSI Level-2A images, we estimate cassava start of season (SOS) across both before and after planting periods on a gridded basis. Establishing meteorological and remote sensing time frames, we aggregate predictive features through different temporal windows. Employing interactive and multistage correlation analysis, along with feature importance ranking, we optimize the selection of predictive features, effectively reducing multicollinearity. The Ensemble model and the usage of combined meteorological and remote sensing optimized predictive features showed best prediction results achieving an overall accuracy of 90%, F1-score of 0.90, Matthews Correlation Coefficient of 0.71, unit-normalized MCC* of 0.85, and an average central actual accuracy of approximately 0.85.Observed CMD patterns demonstrate that soil-crop suitability and farming practices (including the use of most CMD susceptible cultivars) significantly influence the CMD spread in selected farming areas. However, modelling analysis revealed that, among several meteorological features, CMD occurrence is associated with predominant winds blowing across the entire before and after planting periods, mainly from the first quadrant, and to a lesser extent, humidity. Notably, higher disease incidence areas corresponded with SOS events in January and February. In predicting CMD, individual S2-MSI bands were not crucial, but NDVI and EVI emerged as the most explanatory remote sensing features, followed by GNDVI and PSRI indices.The study's findings highlight that coupling CMD occurrence modelling, combined remote sensing and meteorological data with conventional field-based observations may significantly enhance targeted disease management protocols and assessing potential impacts on cassava yields.

The Development of a Specific Nanofiber Bioreceptor for Detection of Escherichia coli and Staphylococcus aureus from Air.

Polluted air and the presence of numerous airborne pathogens affect our daily lives. The sensitive and fast detection of pollutants and pathogens is crucial for environmental monitoring and effective medical diagnostics. Compared to conventional detection methods (PCR, ELISA, metabolic tests, etc.), biosensors bring a very attractive possibility to detect chemicals and organic particles with the mentioned reliability and sensitivity in real time. Moreover, by integrating nanomaterials into the biosensor structure, it is possible to increase the sensitivity and specificity of the device significantly. However, air quality monitoring could be more problematic even with such devices. The greatest challenge with conservative and sensing methods for detecting organic matter such as bacteria is the need to use liquid samples, which slows down the detection procedure and makes it more difficult. In this work, we present the development of a polyacrylonitrile nanofiber bioreceptor functionalized with antibodies against bacterial antigens for the specific interception of bacterial cells directly from the air. We tested the presented novel nanofiber bioreceptor using a unique air filtration system we had previously created. The prepared antibody-functionalized nanofiber membranes for air filtration and pathogen detection (with model organisms E. coli and S. aureus) show a statistically significant increase in bacterial interception compared to unmodified nanofibers. Creating such a bioreceptor could lead to the development of an inexpensive, fast, sensitive, and incredibly selective bionanosensor for detecting bacterial polluted air in commercial premises or medical facilities.

A facile nanozyme-based colorimetric method to realize the quantitative and specific detection of casein phosphopeptides in food samples

As a popular nutritional enhancer, casein phosphopeptides (CPPs) have attracted growing attention in food industry. However, conventional methods for CPPs detection are usually less precise or requires expensive instruments. Herein, a nanozyme-based colorimetric method was developed to achieve the quantitative detection of CPPs in food samples. This method is based on a facilely fabricated peroxidase-like nanozyme (Fe@UiO-66), which combines the specific binding of CPPs, as well as the nanozyme-catalyzed colorimetric sensing that can be easily detected by spectrometer. The method displayed good quantitative ability toward CPPs with the linear range of 2–30 μg/mL, the low limit of detection of 0.267 μg/mL and limit of quantification of 1.335 μg/mL. We highlighted the specificity, anti-interference and practicability of this method, by investigating the performances toward food samples. Besides, a smartphone-based colorimetric sensing platform was also established, which is conducive to the portable detection. The developed nanozyme-based colorimetric sensing method provides a promising strategy for CPPs detection in food samples.

Proactive ransomware prevention in pervasive IoMT via hybrid machine learning

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.

Is it there?—Estimating the invasion of armored sailfin catfish (Pterygoplichthys sp.) in the water bodies of Eastern Ghats, India using the eDNA approach

AbstractEarly detection of invasive species is crucial for effectively controlling the potential damage they can inflict on the ecosystems. In contrast to the many limitations that conventional detection methods like visual surveys and netting hold, environmental DNA assay provides a powerful alternative. This non‐invasive, highly sensitive, and user‐friendly technique offers the advantage of detecting invasive species even in areas where direct observation is challenging, thus addressing the shortcomings of traditional techniques and enhancing overall accuracy in estimating distribution. The spread of invasive Pterygoplichthys sp. has become a cause for concern in biodiversity‐rich countries like India. Despite this, comprehensive studies on the prevalence of this invasive species are limited. The Eastern Ghats of India remain under‐explored with a high potential for supporting diverse lifeforms. Studying the extent of biological invasions in the Eastern Ghats is essential for effective management and to mitigate the ecological and socioeconomic impacts of invasive species. In this study, we have designed and optimized an eDNA‐based quantitative PCR assay to map the presence and spread of invasive Pterygoplichthys sp. in selected freshwater ecosystems of the Eastern Ghats. This assay detected invasive Pterygoplichthys sp. in almost 65% of the total locations sampled. This study can be further extended to larger geographical areas, which can contribute to formulating necessary measures by the authorities to manage invasion and conserve the diversity of the freshwater ecosystem.

Heterogeneous Feature Fusion for Improving Performance of Action Detection

We present a novel framework aimed at improving video action detection through the integration of heterogeneous features. Conventional action detection methods which focus on modeling the relationships between person/object instances rely exclusively on video features and do not exploit valuable intra-instance heterogeneous features, such as person pose, positional information or object category, that can support action recognition. Our proposed framework, termed Heterogeneous Feature Fusion (HFF) framework, addresses this limitation by integrating such intra-instance heterogeneous features for person/object instances, and can improve existing action detection methods. To efficiently exploit each heterogeneous feature, which vary in importance depending on actions and/or scenes, we introduce an attention mechanism to dynamically enhance important heterogeneous features within an instance. Experiments on JHMDB and AVA v2.2 datasets show that our HFF significantly enhances the action detection performance of two existing methods.

An adaptive frequency-hopping detection for slowly-varying fading dispersive channels.

Frequency hopping (FH) signals have been widely used to improve performance against frequency selective fading phenomenon of underwater channels. However, the channel is slowly varying in regard to changes in weather conditions, and thus the conventional FH detection transmitting signals with fixed frequency cannot guarantee good detection performance in the dynamic underwater environment. To overcome the performance degradation in slowly-varying fading dispersive channels, this paper proposes an adaptive frequency-hopping (AFH) target detection method. Compared with conventional FH detection methods, the AFH can adaptively select the optimal detection frequency based on premeasured background noise and channel frequency response measured from previous experiments. Numerical simulations and lake trials are conducted to verify the effectiveness of the AFH. The simulation results show that the AFH has better detection performance than the conventional FH. The lake trial results have also verified the validity and feasibility of AFH. Importantly, AFH also achieves a better output signal-to-noise ratio under actual noise interference.

Exploring new frontiers: cell surface vimentin as an emerging marker for circulating tumor cells and a promising therapeutic target in advanced gastric Cancer

BackgroundCirculating tumor cells (CTCs) hold immense promise in guiding treatment strategies for advanced gastric cancer (GC). However, their clinical impact has been limited due to challenges in identifying epithelial-mesenchymal transition (EMT)-CTCs using conventional methods.MethodsTo bridge this knowledge gap, we established a detection platform for CTCs based on the distinctive biomarker cell surface vimentin (CSV). A prospective study involving 127 GC patients was conducted, comparing CTCs enumeration using both EpCAM and CSV. This approach enabled the detection of both regular and EMT-CTCs, providing a comprehensive analysis. Spiking assays and WES were employed to verify the reliability of this marker and technique. To explore the potential inducer of CSV+CTCs formation, a combination of Tandem Mass Tag (TMT) quantitative proteomics, m6A RNA immunoprecipitation–qPCR (MeRIP–qPCR), single-base elongation- and ligation-based qPCR amplification method (SELECT) and RNA sequencing (RNA-seq) were utilized to screen and confirm the potential target gene. Both in vitro and in vivo experiments were performed to explore the molecular mechanism of CSV expression regulation and its role in GC metastasis.ResultsOur findings revealed the potential of CSV in predicting therapeutic responses and long-term prognosis for advanced GC patients. Additionally, compared to the conventional EpCAM-based CTCs detection method, the CSV-specific positive selection CTCs assay was significantly better for evaluating the therapeutic response and prognosis in advanced GC patients and successfully predicted disease progression 14.25 months earlier than radiology evaluation. Apart from its excellent role as a detection marker, CSV emerges as a promising therapeutic target for attenuating GC metastasis. It was found that fat mass and obesity associated protein (FTO) could act as a potential catalyst for CSV+CTCs formation, and its impact on the insulin-like growth factor-I receptor (IGF-IR) mRNA decay through m6A modification. The activation of IGF-I/IGF-IR signaling enhanced the translocation of vimentin from the cytoplasm to the cell surface through phosphorylation of vimentin at serine 39 (S39). In a GC mouse model, the simultaneous inhibition of CSV and blockade of the IGF-IR pathway yielded promising outcomes.ConclusionIn summary, leveraging CSV as a universal CTCs marker represents a significant breakthrough in advancing personalized medicine for patients with advanced GC. This research not only paves the way for tailored therapeutic strategies but also underscores the pivotal role of CSV in enhancing GC management, opening new frontiers for precision medicine.