Journal Description
Biosensors
Biosensors
is an international, peer-reviewed, open access journal on the technology and science of biosensors published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Embase, CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q1 (Instruments and Instrumentation) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.8 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
5.6 (2024);
5-Year Impact Factor:
5.7 (2024)
Latest Articles
Construction of an Immunosensor Based on the Affinity DNA Functional Ligands to the Fc Segment of IgG Antibody
Biosensors 2025, 15(11), 747; https://doi.org/10.3390/bios15110747 - 5 Nov 2025
Abstract
Over the past few decades, Fc fragment-conjugated proteins, such as Protein A, have been extensively utilized across a range of applications, including antibody purification, site-specific immobilization of antibodies, and the development of biosensing platforms. In this study, building upon our group prior research,
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Over the past few decades, Fc fragment-conjugated proteins, such as Protein A, have been extensively utilized across a range of applications, including antibody purification, site-specific immobilization of antibodies, and the development of biosensing platforms. In this study, building upon our group prior research, we designed and screened an affinity DNA functional ligand (A-DNAFL) and experimentally validated its binding affinity (KD = 6.59 × 10−8) toward mouse IgG antibodies, whose binding performance was comparable to that of protein A. Systematic evaluations were performed to assess the binding efficiency under varying pH levels and ionic strength conditions. Optimal antibody immobilization was achieved in PBST-B buffer under physiological pH 7.2–7.4 and containing approximately 154 mM Na+ and 4 mM K+. Two competitive binding assays confirmed that the A-DNAFL binds to the Fc fragment of murine IgG antibody. Furthermore, molecular docking simulations were employed to investigate the interaction mode, revealing key residues involved in binding as well as the contributions of hydrogen bonding and hydrophobic interactions to complex stabilization. Leveraging these insights, A-DNAFL was utilized as a tool for oriented immobilization of antibodies on the sensing interface, enabling the construction of an immunosensor for ricin detection. Following optimization of immobilization parameters, the biosensor exhibited a detection limit of 30.5 ng/mL with the linear regression equation is lg(Response) = 0.329 lg(Cricin) − 2.027 (N = 9, R = 0.938, p < 0.001)—representing a 64-fold improvement compared to conventional protein A-based methods. The system demonstrated robust resistance to nonspecific interference. Sensing interface reusability was also evaluated, showing only 8.55% signal reduction after two regeneration cycles, indicating that glycine effectively elutes bound antibodies while preserving sensor activity. In summary, the A-DNAFL presented in this study represents a novel antibody-directed immobilization material that serves as a promising alternative to protein A. It offers several advantages, including high modifiability, low production cost, and a relatively small molecular weight. These features collectively contribute to its broad application potential in biosensing, antibody purification, and other areas of life science research.
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(This article belongs to the Section Biosensors and Healthcare)
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Open AccessEditorial
Electrochemical (Bio-) Sensors in Biological Applications—2nd Edition
by
Sergey Shleev, Cecilia Cristea and Nina Dimcheva
Biosensors 2025, 15(11), 746; https://doi.org/10.3390/bios15110746 - 5 Nov 2025
Abstract
The International Union of Pure and Applied Chemistry (IUPAC; https://iupac [...]
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(This article belongs to the Special Issue Electrochemical (Bio-) Sensors in Biological Applications—2nd Edition)
Open AccessArticle
Real-Time Detection of Industrial Respirator Fit Using Embedded Breath Sensors and Machine Learning Algorithms
by
Pablo Aqueveque, Pedro Pinacho-Davidson, Emilio Ramos, Sergio Sobarzo, Francisco Pastene and Anibal S. Morales
Biosensors 2025, 15(11), 745; https://doi.org/10.3390/bios15110745 - 5 Nov 2025
Abstract
Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect
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Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance.
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(This article belongs to the Special Issue Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications)
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Open AccessArticle
Plant Bioelectrical Signals for Environmental and Emotional State Classification
by
Peter A. Gloor
Biosensors 2025, 15(11), 744; https://doi.org/10.3390/bios15110744 - 5 Nov 2025
Abstract
In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we
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In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85–0.86) and recall (0.84–0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability.
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(This article belongs to the Special Issue Biosensing Technology in Agriculture and Biological Products)
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Open AccessArticle
Handheld Lab-on-a-Chip System for Label-Free Dual-Plex Detection of Biomarkers Through On-Chip Plasma Separation
by
Chen-Yuan Chang, Yuan-Pei Lei, Chien Chieh Chiang and Cheng-Sheng Huang
Biosensors 2025, 15(11), 743; https://doi.org/10.3390/bios15110743 - 4 Nov 2025
Abstract
Rapid and reliable detection of biomarkers in complex fluids such as whole blood is essential for effective disease diagnosis and monitoring, particularly in point-of-care settings. Accordingly, this study developed a handheld lab-on-a-chip (LOC) platform that integrates on-chip plasma separation with label-free optical biosensing
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Rapid and reliable detection of biomarkers in complex fluids such as whole blood is essential for effective disease diagnosis and monitoring, particularly in point-of-care settings. Accordingly, this study developed a handheld lab-on-a-chip (LOC) platform that integrates on-chip plasma separation with label-free optical biosensing for real-time, dual-plex detection of biomarkers. The LOC platform includes a two-stage filtration unit that enables efficient separation of plasma from whole blood. This platform also includes a novel gradient grating period guided-mode resonance sensor array that is capable of simultaneously detecting multiple biomarkers with high sensitivity. A compact handheld reader was developed to acquire and analyze optical signals. By using creatinine and albumin as model biomarkers, we demonstrated that the developed platform could achieve sensitive, specific, and reproducible biomarker detection in both plasma and whole-blood samples. The platform can detect albumin and creatinine at concentrations as low as 0.8 and 1.44 μg/mL, respectively, and it exhibits minimal nonspecific binding. These results highlight the potential of the proposed system as a robust and accessible tool for decentralized diagnostics.
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(This article belongs to the Special Issue Development and Application of Integrated Optical Devices in Biosensing)
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Open AccessArticle
Analysis of Olfactive Prints from Artificial Lung Cancer Volatolome with Nanocomposite-Based vQRS Arrays for Healthcare
by
Abhishek Sachan, Mickaël Castro and Jean-François Feller
Biosensors 2025, 15(11), 742; https://doi.org/10.3390/bios15110742 - 4 Nov 2025
Abstract
Exhaled breath analysis is emerging as one of the most promising non-invasive strategies for the early detection of life-threatening diseases, especially lung cancer, where rapid and reliable diagnosis remains a major clinical challenge. In this study, we designed and optimized an electronic nose
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Exhaled breath analysis is emerging as one of the most promising non-invasive strategies for the early detection of life-threatening diseases, especially lung cancer, where rapid and reliable diagnosis remains a major clinical challenge. In this study, we designed and optimized an electronic nose (e-nose) platform composed of quantum resistive vapor sensors (vQRSs) engineered by polymer-carbon nanotube nanocomposites via spray layer-by-layer assembly. Each sensor was tailored through specific polymer functionalization to tune selectivity and enhance sensitivity toward volatile organic compounds (VOCs) of medical relevance. The sensor array, combined with linear discriminant analysis (LDA), demonstrated the ability to accurately discriminate between cancer-related biomarkers in synthetic blends, even when present at trace concentrations within complex volatile backgrounds. Beyond artificial mixtures, the system successfully distinguished real exhaled breath samples collected under challenging conditions, including before and after smoking and alcohol consumption. These results not only validate the robustness and reproducibility of the vQRS-based array but also highlight its potential as a versatile diagnostic tool. Overall, this work underscores the relevance of nanocomposite chemo-resistive arrays for breathomics and paves the way for their integration into future portable e-nose devices dedicated to telemedicine, continuous monitoring, and early-stage disease diagnosis.
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(This article belongs to the Special Issue Electrochemical (Bio-)Sensors in Biological Applications—3rd Edition)
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Open AccessArticle
Microfluidic-Integrated, Ring-Resonator-Assisted Mach–Zehnder Interferometer (μFRA-MZI) as a Label-Free Nanophotonic Sensor
by
Yunju Chang, Ethan Glenn Seutter, Zihao Wang and Jiandi Wan
Biosensors 2025, 15(11), 741; https://doi.org/10.3390/bios15110741 - 4 Nov 2025
Abstract
The ring-assisted Mach–Zehnder interferometer (RA-MZI) has high sensitivity and fast optical response time, and it has been used as a label-free nanophotonic biosensor. Most RA-MZI-based biosensors, however, require chemical modification of the ring surface to immobilize biomolecules that can interact with target molecules
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The ring-assisted Mach–Zehnder interferometer (RA-MZI) has high sensitivity and fast optical response time, and it has been used as a label-free nanophotonic biosensor. Most RA-MZI-based biosensors, however, require chemical modification of the ring surface to immobilize biomolecules that can interact with target molecules for sensing. Here, we report a novel microfluidic-integrated RA-MZI (μFRA-MZI) where a microfluidic channel was fabricated right above the photonic ring resonator. μFRA-MZI allows for direct sample delivery to the RA-MZI without chemical modification of the ring surface and measures shifts in the resonance wavelength induced by the presence of target molecules, enabling label-free detection. In order to optimize the sensitivity of μFRA-MZI, seven devices were fabricated with varied design parameters, including the gap distance between the ring and the bus waveguide (Gring), the length of the multi-mode interferometer (LMMI), and the length of the directional coupler (LDC). Photonic characterization showed that the device with Gring = 1.2 μm, LMMI = 15.5 μm, and LDC = 13.5 μm exhibited the highest extinction ratio (ER) compared to the other six devices, consistent with the simulation-optimized design. Testing with NaCl solutions of varying concentrations yielded a bulk sensitivity of 11.48 nm/refractive index unit (RIU) and an ER of 0.41. With the potential to further improve the device’s sensitivity and the ability to detect samples directly in flow without chemical modifications of the ring resonator, μFRA-MZI will provide a robust and effective approach for label-free biosensing.
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(This article belongs to the Special Issue Design and Application of Microfluidic Biosensors in Biomedicine)
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Open AccessArticle
Machine Learning-Assisted SERS Platform for Rapid and Quantitative Discrimination of Shiga Toxin-Producing E. coli Serotypes
by
Yuting Liu, Jiyu Feng, Xinyi Chen, Mingyu Cheng, Jinglan Zhang, Xu Ye, Yiping Zhao and Bin Ai
Biosensors 2025, 15(11), 740; https://doi.org/10.3390/bios15110740 - 4 Nov 2025
Abstract
Rapid, sensitive, and specific detection of pathogenic Escherichia coli serotypes is crucial for food safety and public health. Here, we present a surface-enhanced Raman scattering (SERS) platform utilizing highly ordered silver nanorod (AgNR) arrays functionalized with vancomycin for efficient and selective bacterial capture.
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Rapid, sensitive, and specific detection of pathogenic Escherichia coli serotypes is crucial for food safety and public health. Here, we present a surface-enhanced Raman scattering (SERS) platform utilizing highly ordered silver nanorod (AgNR) arrays functionalized with vancomycin for efficient and selective bacterial capture. The system enables multiplexed, high-throughput analysis using a portable Raman spectrometer, achieving direct molecular fingerprinting of seven clinically relevant E. coli serotypes. Systematic optimization of AgNR length and vancomycin coating maximized SERS enhancement and capture efficiency. Advanced data analysis with linear discriminant analysis (LDA) provided robust discrimination among all serotypes and concentrations, achieving up to 100% classification accuracy in single-concentration models and an overall accuracy of 98.41% when all concentrations and serotypes were evaluated jointly. This integrated SERS approach demonstrates significant promise for rapid, on-site bacterial diagnostics and quantitative pathogen monitoring, paving the way for practical applications in food safety and clinical microbiology.
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(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in Biosensors: Innovation, Application, and Challenge)
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Open AccessArticle
Electrochemical Tracking of Macrophage Migration Inhibitory Factor: A Leap Toward Precision Colorectal Cancer Diagnosis and Prognosis
by
Eloy Povedano, Antonino-Biagio Carbonaro, Verónica Serafín, María Gamella, Alessandro Giuffrida, Ana Montero-Calle, José Manuel Pingarrón, Rodrigo Barderas and Susana Campuzano
Biosensors 2025, 15(11), 739; https://doi.org/10.3390/bios15110739 - 4 Nov 2025
Abstract
Colorectal cancer (CRC) remains a significant global health burden, mainly due to late diagnosis and chemotherapy resistance. Macrophage migration inhibitory factor (MIF), a proinflammatory cytokine associated with tumor progression, has emerged as a promising biomarker in CRC. However, its clinical utility is limited
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Colorectal cancer (CRC) remains a significant global health burden, mainly due to late diagnosis and chemotherapy resistance. Macrophage migration inhibitory factor (MIF), a proinflammatory cytokine associated with tumor progression, has emerged as a promising biomarker in CRC. However, its clinical utility is limited by the lack of rapid and accessible detection methods. In this study, we report an electrochemical immunotechnology for the sensitive and selective quantification of MIF protein in CRC tissue samples. By combining magnetic microparticles (MMPs), antibody-based recognition, horseradish peroxidase (HRP) labeling, and amperometric transduction at disposable screen-printed carbon electrodes (SPCEs), the developed methodology displayed a linear dynamic range from 0.24 to 20 ng mL−1, enabling quantification across clinically relevant MIF levels, and achieving a low limit of detection (0.07 ng mL−1). In addition, the developed method is the only one reported for MIF assembled on MMPs and addresses its determination in a relevant oncological scenario (paired non-tumoral (NT) and tumoral (T) tissues from individuals diagnosed with CRC at different stages of the disease). The analysis, requiring only 100 ng of tissue extract, allowed efficient discrimination between NT and T paired tissues, and successfully differentiated between healthy, early (I–II) and advanced (III–IV) CRC stages, achieving these results in just 105 min.
Full article
(This article belongs to the Special Issue In Honor of Prof. Evgeny Katz: Biosensors: Science and Technology)
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Open AccessArticle
Evaluating the Performance of Hyperspectral Imaging Endoscopes: Mitigating Parameters Affecting Spectral Accuracy
by
Siavash Mazdeyasna, Mohammed Shahriar Arefin, Andrew Fales, Silas J. Leavesley, T. Joshua Pfefer and Quanzeng Wang
Biosensors 2025, 15(11), 738; https://doi.org/10.3390/bios15110738 - 4 Nov 2025
Abstract
Hyperspectral imaging (HSI) is increasingly used in studies for medical applications as it provides both structural and functional information of biological tissue, enhancing diagnostic accuracy and clinical decision-making. Recently, HSI cameras (HSICs) have been integrated with medical endoscopes (HSIEs), capturing hypercube data beyond
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Hyperspectral imaging (HSI) is increasingly used in studies for medical applications as it provides both structural and functional information of biological tissue, enhancing diagnostic accuracy and clinical decision-making. Recently, HSI cameras (HSICs) have been integrated with medical endoscopes (HSIEs), capturing hypercube data beyond conventional white light imaging endoscopes. However, there are currently no cleared or approved HSIEs by the U.S. Food and Drug Administration (FDA). HSI accuracy depends on technologies and experimental parameters, which must be assessed for reliability. Importantly, the reflectance spectrum of a target can vary across different cameras and under different environmental or operational conditions. Thus, before reliable clinical translation can be achieved, a fundamental question must be addressed: can the same target yield consistent spectral measurements across different HSI systems and under varying acquisition conditions? This study investigates the impact of eight parameters—ambient light, exposure time, camera warm-up time, spatial and temporal averaging, camera focus, working distance, illumination angle, and target angle—on spectral measurements using two HSI techniques: interferometer-based spectral scanning and snapshot. Controlled experiments were conducted to evaluate how each parameter affects spectral accuracy and whether normalization can mitigate these effects. Our findings reveal that several parameters significantly influence spectral measurements, with some having a more pronounced impact. While normalization reduced variations for most parameters, it was less effective at mitigating errors caused by ambient light and camera warm-up time. Additionally, normalization did not eliminate spectral noise resulting from low exposure time, small region of interest, or a spectrally non-uniform light source. From these results, we propose practical considerations for optimizing HSI system performance. Implementing these measures can minimize variations in reflectance spectra of identical targets captured by different cameras and under diverse conditions, thereby supporting the reliable translation of HSI techniques to clinical applications.
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(This article belongs to the Special Issue Advanced Optical Imaging Biosensors: Technologies and Biomedical Applications)
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Open AccessReview
Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends
by
Mi-Ran Ki, Dong Hyun Kim, Mohamed A. A. Abdelhamid and Seung Pil Pack
Biosensors 2025, 15(11), 737; https://doi.org/10.3390/bios15110737 - 4 Nov 2025
Abstract
Cancer and aging are two distinct biological processes with shared cellular pathways, such as cellular senescence, DNA damage repair, and metabolic reprogramming. However, the outcomes of these processes differ in terms of proliferation. Understanding biomarkers related to aging and cancer opens a pathway
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Cancer and aging are two distinct biological processes with shared cellular pathways, such as cellular senescence, DNA damage repair, and metabolic reprogramming. However, the outcomes of these processes differ in terms of proliferation. Understanding biomarkers related to aging and cancer opens a pathway for therapeutic interventions and more effective prevention, detection, and treatment strategies. Biomarkers, ranging from molecular to phenotypic indicators, play an important role in early detection, risk assessment, and prognosis in this endeavor. This review comprehensively examines key biomarkers associated with cancer and aging, highlighting their importance in early diagnostic strategies. The review discusses recent advances in biomarker-based diagnostic technologies, such as liquid biopsy, multi-omics integration, and artificial intelligence, and emphasizes their novel potential for early detection, accurate risk assessment, and personalized therapeutic interventions in cancer and aging science. We also explore the current state of biosensor development and clinical application cases. Finally, we discuss the limitations of current early diagnostic methods and propose future research directions to enhance biomarker-based diagnostic technologies.
Full article
(This article belongs to the Special Issue Applications of Cutting-Edge Biosensors in Environment, Food and Healthcare Field)
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Open AccessArticle
Aggregation-Induced Emission-Based Lateral Flow Immunoassay for Ultra-Sensitive and On-Site Detection of Porcine Epidemic Diarrhea Virus
by
Bin Wang, Xufei Feng, Qian He, Hongwei Shi, Wei Hou, Jianjun Geng and Haidong Wang
Biosensors 2025, 15(11), 736; https://doi.org/10.3390/bios15110736 - 3 Nov 2025
Abstract
The porcine epidemic diarrhea virus (PEDV) has inflicted substantial economic losses on the swine industry, underscoring the need for sensitive point-of-care diagnostics. While lateral flow immunoassays (LFIA) offer rapidity and ease of use, traditional labels like colloidal gold suffer from limited sensitivity. Herein,
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The porcine epidemic diarrhea virus (PEDV) has inflicted substantial economic losses on the swine industry, underscoring the need for sensitive point-of-care diagnostics. While lateral flow immunoassays (LFIA) offer rapidity and ease of use, traditional labels like colloidal gold suffer from limited sensitivity. Herein, we developed an aggregation-induced emission (AIE)-based LFIA, termed PED-ALFIA, for the highly sensitive detection of the PEDV antigen. PED-ALFIA exhibited a detection limit of 2.44 × 102 TCID50/mL, which represents a 256-fold improvement in sensitivity over commercial colloidal gold kits and a 4-fold enhancement compared to our previously developed PED-TRFIA. The assay showed no cross-reactivity with other common swine viruses and demonstrated high reproducibility. When tested on clinical samples (n = 42), results showed 100% concordance with qPCR. Utilizing a portable fluorescence reader, the assay can be completed within 10 min, establishing PED-ALFIA as a sensitive, specific, and rapid on-site tool for the early diagnosis of PEDV.
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(This article belongs to the Section Biosensors and Healthcare)
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Open AccessArticle
Novel Nanocomposites of Carbon Nanomaterials and Poly(Neutral Red) Electropolymerized from Reline for DNA Damage Detection and Beverage Antioxidant Influence Assessment
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Anastasia Malanina, Rufiia Derbisheva, Tatiana Krasnova, Rezeda Shamagsumova, Vladimir Evtugyn, Alexey Ivanov and Anna Porfireva
Biosensors 2025, 15(11), 735; https://doi.org/10.3390/bios15110735 - 3 Nov 2025
Abstract
Novel nanocomposites based on carbon black or multi-walled carbon nanotubes functionalized with carboxylic groups and Neutral red electropolymerized from reline were obtained in a one-step protocol and used for DNA biosensor development. The synthesis was carried out in potentiodynamic mode in a deep
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Novel nanocomposites based on carbon black or multi-walled carbon nanotubes functionalized with carboxylic groups and Neutral red electropolymerized from reline were obtained in a one-step protocol and used for DNA biosensor development. The synthesis was carried out in potentiodynamic mode in a deep eutectic solvent reline consisting of a mixture of choline chloride and urea. The nanocomposite based on carbon black and poly(Neutral red) was applied for a voltammetric DNA biosensor developed to discriminate DNA damage. The sensor developed allowed the native, thermally denatured, and chemically oxidized DNA discrimination with either current changes or peak potential shifts. The nature of the DNA used had affected the sensor’s analytical response value. The DNA biosensor suggested was tested for the assessment of antioxidant capacity in such beverages as tea, coffee, white wine, and fruit-based drink purchased from local market. Simple, fast, and inexpensive approach of sensor modifying layer assembly would be demanded in control of food products and beverages quality, as well as for medical purposes.
Full article
(This article belongs to the Special Issue Nanotechnology Biosensing in Bioanalysis and Beyond)
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Open AccessReview
Wearable Sensors for Precise Exercise Monitoring and Analysis
by
Bo Su, Fengyu Li and Bingtian Su
Biosensors 2025, 15(11), 734; https://doi.org/10.3390/bios15110734 - 3 Nov 2025
Abstract
The adoption of wearable sensors for precision training has accelerated in recent years, yet most studies and reviews remain device- or feasibility-centric and lack a field-ready decision framework. This review organizes wearable sensing across four monitoring dimensions—physiological, kinematic, biochemical, and dynamic—and maps them
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The adoption of wearable sensors for precision training has accelerated in recent years, yet most studies and reviews remain device- or feasibility-centric and lack a field-ready decision framework. This review organizes wearable sensing across four monitoring dimensions—physiological, kinematic, biochemical, and dynamic—and maps them onto three training pillars: physical, technical, and tactical. From the perspectives of athletes and coaches, we operationalize quality control, threshold, and feedback loop to translate measurement into action. We critically appraise key limitations, including signal robustness under high-intensity motion, inter-individual variability and limited model generalizability, cross-device data fusion and latency, battery life and wearability, privacy and data ownership, and limited accessibility beyond elite settings. Looking ahead, we advocate a shift from mere multidimensional measurement to a verifiable, reusable, and deployable precision-training ecosystem that delivers actionable metrics and clear decision support for practitioners.
Full article
(This article belongs to the Special Issue Wearable Biosensors and Health Monitoring)
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Open AccessArticle
Estimating Toxicity Putative Mechanisms from Smoking Residual Substances Using a Whole-Cell Bioreporter System
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Tal Bar, Marilou Shagan, Esti Kramarsky-Winter, Robert S. Marks, Karina Golberg and Ariel Kushmaro
Biosensors 2025, 15(11), 733; https://doi.org/10.3390/bios15110733 - 3 Nov 2025
Abstract
Cigarette smoking is known to be an unhealthy activity that can cause a number of human diseases, including chronic obstructive pulmonary disease (COPD) and lung cancer. It was further reported that even being exposed to secondhand cigarette smoke can affect human health. To
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Cigarette smoking is known to be an unhealthy activity that can cause a number of human diseases, including chronic obstructive pulmonary disease (COPD) and lung cancer. It was further reported that even being exposed to secondhand cigarette smoke can affect human health. To assess the toxicity of the smoke from different cigarette brands, an artificial smoking device was developed, and three fractions designated, Filter Fraction, Smoke Fraction and Tar Fraction, were prepared from the smoke of each brand. Then, to elucidate possible effects of some of the toxins found in cigarette smoke, we investigated their effects in vitro using a bioluminescent bacterial array that comprises three bacterial strains. Using this array, we compare smoke from three cigarette brands, each with different tar and nicotine contents. GC-MS analysis showed that the cigarette smoke extracts (fractions) from different brands differed in their compositions and chemical concentrations. The results further showed that, in general, cigarette smoke triggered mainly an oxidative stress reaction in our bacterial models. The Smoke Fraction was tested for sequential smoking rounds and found to produce cumulative effects following each subsequent smoking cycle for all three cigarette brands. Finally, it was found that cigarette smoke and its specific components are toxic at various degrees with the Smoke Fraction, acting as oxidative stressors, and that this can be effectively analyzed using bioreporter panel arrays.
Full article
(This article belongs to the Special Issue Feature Paper in Biosensor and Bioelectronic Devices 2025)
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Open AccessArticle
End of Apnea Event Prediction Leveraging EEG Signals and Interpretable Machine Learning
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Hisham ElMoaqet, Abdullah Ahmed, Mutaz Ryalat, Natheer Almtireen, Matthew Salanitro, Martin Glos and Thomas Penzel
Biosensors 2025, 15(11), 732; https://doi.org/10.3390/bios15110732 - 2 Nov 2025
Abstract
Obstructive sleep apnea is a prevalent sleep disorder with serious health implications. While previous studies focused on detecting apnea events, little is known about the factors that determine whether an apnea episode continues or terminates. Understanding these mechanisms is crucial for optimizing treatment
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Obstructive sleep apnea is a prevalent sleep disorder with serious health implications. While previous studies focused on detecting apnea events, little is known about the factors that determine whether an apnea episode continues or terminates. Understanding these mechanisms is crucial for optimizing treatment strategies. In this study, we analyzed 30-s brain activity segments during continuous and ending apnea events to identify neurophysiological markers of event termination, with particular emphasis on the most influential EEG features. Frequency-domain and complexity features were extracted, and several ensemble machine learning models were trained and evaluated. Our results show that the Extra Trees model achieved the highest performance, with an accuracy of 0.88, F1-score for ending apnea of 0.87, and an area under the receiver operating characteristic curve of 0.95. Feature importance analyses and SHAP visualizations highlighted frequency-band energy, Teager–Kaiser energy, and signal complexity as key contributors. Temporal analyses revealed how these features evolve during apnea termination. These findings suggest that cortical activation and transient arousal processes play a decisive role in ending apnea events and may facilitate the development of more advanced adaptive or closed-loop sleep apnea therapies.
Full article
(This article belongs to the Special Issue Portable Bioelectronic Devices for Telemedicine, Healthcare and Sports Applications)
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Open AccessArticle
An Accumulation Pretreatment-Free POCT Biochip for Visual and Sensitive ABO/Rh Blood Cell Typing
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Pengcheng Wang, Mingdi He, Yan Ma, Yunhuang Yang and Rui Hu
Biosensors 2025, 15(11), 731; https://doi.org/10.3390/bios15110731 - 2 Nov 2025
Abstract
Rapid blood type detection in point-of-care testing (POCT) scenarios is crucial for various clinical treatments. In this study, we present a sensitive, cost-effective, and straightforward biosensing approach for visual blood typing that notably simplifies the procedure by eliminating any need for blood sample
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Rapid blood type detection in point-of-care testing (POCT) scenarios is crucial for various clinical treatments. In this study, we present a sensitive, cost-effective, and straightforward biosensing approach for visual blood typing that notably simplifies the procedure by eliminating any need for blood sample pretreatment. Our technique achieves this by directly trapping and accumulating red blood cell (RBC) clusters within a photolithography-based microfluidic chip, thereby bypassing complex preprocessing. By employing an antigen–antibody assay involving isoagglutinins A, B, and/or D on the RBC surface and their corresponding antibodies, we effectively determine blood types. When antibodies are present, the corresponding RBCs bind to the antibody-conjugated RBC clusters, which are subsequently trapped within the microfluidic accumulation chip, resulting in the formation of a visible bar. The blood group can then be readily identified by observing this visual bar with the naked eye or under microscopy. Notably, we integrate two continuous mixing units (Z and S) at the entrance of the biochip to improve mixing efficiency and accelerate the antigen–antibody interaction. This method demonstrates high selectivity, accuracy, and stability across various clinical blood samples. Moreover, the sensor operates with minimal sample volume (as low as 10 μL) and delivers results within 5 min. The fabrication cost of the PDMS-based biochip is approximately $0.2 per chip, and the limit of detection (LOD) is determined to be 3 × 106 cells/mL, indicating excellent sensitivity and affordability for practical use. Overall, this biochip provides a fast, low-cost, and reliable solution for emergency blood typing, particularly in resource-limited settings.
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(This article belongs to the Special Issue Translational Micro- and Nanofluidic Technologies for Clinical Diagnostics: From Development to Real-World Applications)
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Open AccessReview
ZnS-Based Electrode Materials for Electrochemical Sensing (Environmental Monitoring and Food Samples) and Energy Storage Applications
by
Chellakannu Rajkumar, Shanmugam Vignesh, Khursheed Ahmad and Tae Hwan Oh
Biosensors 2025, 15(11), 730; https://doi.org/10.3390/bios15110730 - 2 Nov 2025
Abstract
In the present scenario, it is believed that the fabrication of cost-effective and environmentally friendly nanomaterials is of great significance for various optoelectronic and electrochemical applications. In the past few years, zinc sulfide and its composites with carbon-based materials, metal oxides, MXenes, metal–organic
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In the present scenario, it is believed that the fabrication of cost-effective and environmentally friendly nanomaterials is of great significance for various optoelectronic and electrochemical applications. In the past few years, zinc sulfide and its composites with carbon-based materials, metal oxides, MXenes, metal–organic frameworks (MOFs) and other materials have been prepared for electrochemical applications. The ZnS-based materials exhibit good specific surface area, catalytic activity, and decent conductivity, which makes them promising materials for sensors and supercapacitors (SCs). In this review article, we briefly discuss the synthesis of ZnS using various methods, such as hydrothermal, microwave, sol–gel, electrochemical, and ultrasonication methods. Furthermore, ZnS and its composites for electrochemical sensors are reviewed. The limits of detection, sensitivity, stability, and selectivity of the reported sensors are discussed. Furthermore, studies based on ZnS and its composites for SC applications are reviewed. It was found that ZnS-based composites exhibit good electrochemical performance for SCs. The limitations and prospects of ZnS-based materials are also discussed. We believe that the present review article may be useful for researchers who are involved in the fabrication of ZnS-based materials for SCs and electrochemical sensing applications.
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(This article belongs to the Special Issue Biosensors for Environmental Monitoring and Food Safety)
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Open AccessSystematic Review
Integrating AI with Biosensors and Voltammetry for Neurotransmitter Detection and Quantification: A Systematic Review
by
Ibrahim Moubarak Nchouwat Ndumgouo, Mohammad Zahir Uddin Chowdhury, Silvana Andreescu and Stephanie Schuckers
Biosensors 2025, 15(11), 729; https://doi.org/10.3390/bios15110729 - 2 Nov 2025
Abstract
Background: The accurate and timely diagnosis of neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and major depressive disorder critically depends on real-time monitoring and precise interpretation of authentic neurotransmitter (NT) signal dynamics in complex biological fluids (CBFs), including cerebrospinal fluid. These NT
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Background: The accurate and timely diagnosis of neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and major depressive disorder critically depends on real-time monitoring and precise interpretation of authentic neurotransmitter (NT) signal dynamics in complex biological fluids (CBFs), including cerebrospinal fluid. These NT dynamics are governed by both the type and concentration of NTs present in the CBFs. However, current biosensors face significant limitations in sensitivity and selectivity, thereby hindering reliable estimation (detection and quantification) of NTs. Though nanomaterials and bioenzymes have been utilized to modify sensor interfaces for enhanced performance, issues like signal convolution, electrode fouling, and inter-NT crosstalk persist. Objectives: This review aims to evaluate and synthesize current research on the use of artificial intelligence (AI), particularly machine learning (ML), pattern recognition (PR), and deep learning (DL), to improve the automated detection and quantification of neurotransmitters from complex biological fluids. Design: A systematic review of 33 peer-reviewed studies was conducted, focusing on the integration of AI methods in neurotransmitter estimation. The review includes an analysis of commonly studied NTs, the methodologies for their detection, data acquisition techniques, and the AI algorithms applied for signal processing and interpretation. Results: The studies reviewed demonstrate that AI-based approaches have shown considerable potential in overcoming traditional biosensor limitations by effectively deconvoluting complex, multiplexed NT signals. These techniques allow for more accurate NT estimation in real-time monitoring scenarios. The review categorizes AI methodologies by their application and performance in NT signal analysis. Conclusions: AI-enhanced NT monitoring represents a promising direction for advancing diagnostic and therapeutic capabilities in neurodegenerative diseases. Despite current challenges, such as sensor stability and NT interaction complexity, AI integration, particularly in applications like closed-loop deep brain stimulation (CLDBS), offers significant potential for more effective and personalized treatments.
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(This article belongs to the Special Issue In Honor of Prof. Evgeny Katz: Biosensors: Science and Technology)
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Open AccessArticle
Tungsten Oxide-Mediated Photocatalytic Silver Enhancement in a QCM Immunosensor for Alpha-Fetoprotein Detection
by
Han Sol Kim, Yu Gyeong Cho and Soo Suk Lee
Biosensors 2025, 15(11), 728; https://doi.org/10.3390/bios15110728 - 2 Nov 2025
Abstract
Accurate and early detection of alpha-fetoprotein (AFP) in human serum is essential for the diagnosis and monitoring of hepatocellular carcinoma and related diseases. In this study, we present a highly sensitive and reproducible quartz crystal microbalance (QCM) immunosensor for the quantitative detection of
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Accurate and early detection of alpha-fetoprotein (AFP) in human serum is essential for the diagnosis and monitoring of hepatocellular carcinoma and related diseases. In this study, we present a highly sensitive and reproducible quartz crystal microbalance (QCM) immunosensor for the quantitative detection of AFP. The detection strategy is based on a sandwich-type immunoassay coupled with a signal amplification method utilizing photocatalytic silver deposition on tungsten(IV) oxide (WO3) nanoparticles. Since QCM detects resonance frequency shifts induced by mass changes on the sensor surface, the silver-enhanced growth of WO3 nanoparticles enables significant signal amplification, allowing for precise mass-based quantification. Without amplification, the limit of detection (LOD) for AFP using the QCM immunosensor was 286 pg/mL, which was significantly improved to 43.7 pg/mL with photocatalytic silver staining. This approach markedly improves both sensitivity and reproducibility of the assay, offering a robust and efficient platform for clinical biomarker detection and early cancer diagnostics.
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(This article belongs to the Special Issue Nanomaterial-Based Biosensors for Point-of-Care Testing)
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