Biosensors

44 pages, 3667 KB

Open AccessReview

Engineered Protein Modification: A New Paradigm for Enhancing Biosensing Sensitivity and Diagnostic Accuracy

by Zheng Xu, Chu Wang, Ziting Zhang, Heng Wang, Peiyi Gao and Lixing Weng

Biosensors 2026, 16(1), 21; https://doi.org/10.3390/bios16010021 (registering DOI) - 26 Dec 2025

Protein modifications, particularly post-translational modifications (PTMs) such as phosphorylation and glycosylation, are fundamental mechanisms regulating cellular activity and disease pathogenesis, with their detection emerging as a promising frontier for advanced diagnostics. This review systematically examines the integration of engineered protein modifications with biosensing [...] Read more.

Protein modifications, particularly post-translational modifications (PTMs) such as phosphorylation and glycosylation, are fundamental mechanisms regulating cellular activity and disease pathogenesis, with their detection emerging as a promising frontier for advanced diagnostics. This review systematically examines the integration of engineered protein modifications with biosensing technologies to enhance analytical performance and diagnostic accuracy. Through critical analysis of current methodologies, we highlight how strategic manipulation of PTMs improves biosensor sensitivity and specificity in applications ranging from early disease detection to environmental monitoring. The analysis identifies significant advancements in detection platforms while acknowledging persistent challenges in real-world integration and standardization. We conclude that optimizing protein modification-based sensing strategies represents a crucial pathway for developing robust, clinically translatable diagnostic tools, and propose focused research directions to address existing technical barriers and accelerate practical implementation. Full article

(This article belongs to the Special Issue Biomedical Applications of Smart Sensors)

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61 pages, 2378 KB

Open AccessReview

Active Rehabilitation Technologies for Post-Stroke Patients

by Hongbei Meng, Zihe Zhao, Shangru Li, Shengbo Wang, Jiacheng Wang, Canxi Yang, Chenyu Tang, Xuhang Chen, Xiaoxue Zhai, Yu Pan, Arokia Nathan, Peter Smielewski, Luigi G. Occhipinti and Shuo Gao

Biosensors 2026, 16(1), 20; https://doi.org/10.3390/bios16010020 - 25 Dec 2025

Abstract

Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active [...] Read more.

Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active rehabilitation methods can respond to patients’ motor intentions in real-time and significantly increase motivation and engagement, leading to efficient utilization of critical recovery windows and better rehabilitation outcomes. In this review, we focus on the physiological basis of active rehabilitation, including mechanisms of neuroplasticity, and discuss recent advances in intent detection and feedback devices. We also examine treatment options for different stages of stroke recovery, providing a comprehensive reference for engineers to design optimized rehabilitation techniques and for clinicians to select appropriate rehabilitation protocols. These developments create new opportunities to improve the lives of stroke patients and offer greater hope for their recovery. Full article

(This article belongs to the Special Issue Development Trends of AI-Enabled Biomedical Biosensors)

15 pages, 4263 KB

Open AccessArticle

Flexible Cu Nanostructured Laser-Induced Graphene Electrodes for Highly Sensitive and Non-Invasive Lactate Detection in Saliva

by Anju Joshi and Gymama Slaughter

Biosensors 2026, 16(1), 19; https://doi.org/10.3390/bios16010019 - 25 Dec 2025

Abstract

A scalable and facile fabrication strategy is presented for developing a flexible, nanostructured, non-enzymatic electrochemical sensor for lactate detection based on copper-modified laser-induced graphene (CuNPs/LIG). A one-step electrodeposition process was employed to uniformly decorate the porous LIG framework with copper nanostructures, offering a [...] Read more.

A scalable and facile fabrication strategy is presented for developing a flexible, nanostructured, non-enzymatic electrochemical sensor for lactate detection based on copper-modified laser-induced graphene (CuNPs/LIG). A one-step electrodeposition process was employed to uniformly decorate the porous LIG framework with copper nanostructures, offering a cost-effective and reproducible approach for constructing enzyme-free sensing platforms. Scanning electron microscopy and energy-dispersive X-ray spectroscopy confirmed dense Cu nanostructure loading and efficient interfacial integration across the conductive LIG surface. The resulting CuNPs/LIG electrode exhibited excellent electrocatalytic performance, achieving a sensitivity of 8.56 μA µM⁻¹ cm⁻² with a low detection limit of 42.65 μM and a linear response toward lactate concentrations ranging from 100 to 1100 μM in artificial saliva under physiological conditions. The sensor maintained high selectivity in the presence of physiologically relevant interferents. Practical applicability was demonstrated through recovery studies, where recovery rates exceeding 104% showcase the sensor’s analytical reliability in complex biological matrices. Overall, this work establishes a robust, sensitive, and cost-efficient Cu-nanostructured LIG sensing platform, offering strong potential for non-invasive lactate monitoring in real-world biomedical and wearable applications. Full article

(This article belongs to the Special Issue Aptamer-Based Biosensors for Point-of-Care Diagnostics—2nd Edition)

15 pages, 1468 KB

Open AccessArticle

AI-Assisted Impedance Biosensing of Yeast Cell Concentration

by Amir A. AlMarzooqi, Mahmoud Al Ahmad, Jisha Chalissery and Ahmed H. Hassan

Biosensors 2026, 16(1), 18; https://doi.org/10.3390/bios16010018 - 25 Dec 2025

Abstract

Quantifying microbial growth with high temporal resolution remains essential yet challenging due to limitations of optical, manual, and biochemical methods. Here, we introduce an AI-enhanced electrochemical impedance spectroscopy platform for real-time, label-free monitoring of Saccharomyces cerevisiae growth. Broadband impedance measurements (1 Hz–100 kHz) [...] Read more.

Quantifying microbial growth with high temporal resolution remains essential yet challenging due to limitations of optical, manual, and biochemical methods. Here, we introduce an AI-enhanced electrochemical impedance spectroscopy platform for real-time, label-free monitoring of Saccharomyces cerevisiae growth. Broadband impedance measurements (1 Hz–100 kHz) were collected from yeast cultures across log-phase development. Engineered features—derived from impedance magnitude and phase—captured dielectric and conductive shifts associated with cell proliferation, membrane polarization, and ionic redistribution. A Gaussian Process Regression model trained on these features predicted optical density (OD600) with high precision (RMSE = 0.79 min; R² = 0.9996; r = 0.9998), and achieved 100% classification accuracy when discretized into 15-min growth intervals. The system operated with sub-millisecond latency and minimal memory footprint, enabling embedded deployment. Benchmarking against conventional methods revealed superior throughput, automation potential, and independence from labeling or turbidity-based optics. This AI-driven platform forms the core of a real-time digital twin for yeast culture monitoring, capable of predictive tracking and adaptive control. By fusing electrochemical biosensing with machine learning, our method offers a scalable and robust solution for intelligent fermentation and bioprocess optimization. Full article

(This article belongs to the Section Biosensor and Bioelectronic Devices)

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14 pages, 2279 KB

Open AccessArticle

Engineering a CRISPR-Mediated Dual Signal Amplification-Based Biosensor for miRNA Determination

by Zhixian Liang, Jie Zhang and Shaohui Zhang

Biosensors 2026, 16(1), 17; https://doi.org/10.3390/bios16010017 - 24 Dec 2025

Abstract

MicroRNAs, pivotal regulators of gene expression and physiology, serve as reliable biomarkers for early cancer diagnosis and therapy. As one of the earliest discovered miRNAs in the human genome, miRNA-21 provides critical information for early cancer diagnosis, drug therapy, and prognosis. In this [...] Read more.

MicroRNAs, pivotal regulators of gene expression and physiology, serve as reliable biomarkers for early cancer diagnosis and therapy. As one of the earliest discovered miRNAs in the human genome, miRNA-21 provides critical information for early cancer diagnosis, drug therapy, and prognosis. In this work, we harness CRISPR as a bridge to integrate target-induced self-priming hairpin isothermal amplification (SIAM) with terminal transferase (TdT) polymerization labeling, constructing a facile, straightforward electrochemical biosensor for sensitive miRNA-21 detection. Unlike conventional single-strand template-based exponential amplification (EXPAR), the SIAM hairpin undergoes target triggered intramolecular conformational change, initiating extension and strand displacement reactions that suppress nonspecific dimer formation and lower background current. Notably, the assay requires only a single probe, enabling unidirectional signal amplification while nonspecific reactions caused by system complexity. The generated SIAM products activate the Cas12a/crRNA complex to trans-cleave PO₄³⁻ modified single-stranded DNAs (ssDNAs); the resulting 3′ hydroxyl ssDNAs are subsequently labeled by TdT, with the assistance of SA-HRP catalyzing hydrogen peroxide, achieving robust signal amplification. Under optimized conditions, the cathodic current exhibits a logarithmic relationship with miRNA concentrations from 20 fM to 5.0 × 10⁸ fM, with a detection limit of 9.2 fM. The biosensor successfully quantified miRNA-21 in commercial serum samples and biological lysates, demonstrating its potential for cancer diagnostics and therapy. Full article

(This article belongs to the Special Issue CRISPR/Cas System-Based Biosensors)

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24 pages, 20297 KB

Open AccessReview

Artificial Intelligence-Aided Microfluidic Cell Culture Systems

by Muhammad Sohail Ibrahim and Minseok Kim

Biosensors 2026, 16(1), 16; https://doi.org/10.3390/bios16010016 - 24 Dec 2025

Abstract

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid [...] Read more.

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research. Full article

(This article belongs to the Collection Microsystems for Cell Cultures)

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23 pages, 581 KB

Open AccessSystematic Review

Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review

by Faisal Mehmood, Sajid Ur Rehman, Asif Mehmood and Young-Jin Kim

Biosensors 2026, 16(1), 15; https://doi.org/10.3390/bios16010015 - 24 Dec 2025

Abstract

Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis [...] Read more.

Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment. Full article

(This article belongs to the Special Issue Latest Wearable Biosensors—2nd Edition)

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34 pages, 11909 KB

Open AccessReview

Emerging Microfluidic Plasma Separation Technologies for Point-of-Care Diagnostics: Moving Beyond Conventional Centrifugation

by Ergun Alperay Tarim, Michael G. Mauk and Mohamed El-Tholoth

Biosensors 2026, 16(1), 14; https://doi.org/10.3390/bios16010014 - 23 Dec 2025

Abstract

Plasma separation is an essential step in blood-based diagnostics. While traditional centrifugation is effective, it is costly and usually restricted to centralized laboratories because it requires relatively expensive equipment, a supply of consumables, and trained personnel. In an effort to alleviate these shortcomings, [...] Read more.

Plasma separation is an essential step in blood-based diagnostics. While traditional centrifugation is effective, it is costly and usually restricted to centralized laboratories because it requires relatively expensive equipment, a supply of consumables, and trained personnel. In an effort to alleviate these shortcomings, microfluidic and point-of-care devices offering rapid and low-cost plasma separation from small sample volumes, such as finger-stick samples, are quickly emerging as an alternative. Such microscale plasma separation systems enable reduced costs, rapid test results, self-testing, and broader accessibility, particularly in resource-limited or remote settings and facilitate the integration of separation, fluid handling, and downstream analysis into portable, automated lab-on-a-chip platforms. This review highlights advances in microfluidic systems and lab-on-a-chip devices for plasma separation categorized in design strategies, separation principles and characteristics, application purposes, and future directions for the decentralization of healthcare and personalized medicine. Full article

(This article belongs to the Special Issue Advanced Microfluidic Devices and Lab-on-Chip (Bio)sensors)

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17 pages, 42077 KB

Open AccessArticle

Noninvasive Sensing of Foliar Moisture in Hydroponic Crops Using Leaf-Based Electric Field Energy Harvesters

by Oswaldo Menéndez-Granizo, Alexis Chugá-Portilla, Tito Arevalo-Ramirez, Juan Pablo Vásconez, Fernando Auat-Cheein and Álvaro Prado-Romo

Biosensors 2026, 16(1), 13; https://doi.org/10.3390/bios16010013 - 23 Dec 2025

Abstract

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To [...] Read more.

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of

R^{2} = 0.697

and

R^{2} = 0.794

for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops. Full article

(This article belongs to the Section Environmental Biosensors and Biosensing)

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18 pages, 3675 KB

Open AccessArticle

Highly Sensitive Biosensor for the Detection of Cardiac Troponin I in Serum via Surface Plasmon Resonance on Polymeric Optical Fiber Functionalized with Castor Oil-Derived Molecularly Imprinted Nanoparticles

by Alice Marinangeli, Pinar Cakir Hatir, Mustafa Baris Yagci and Alessandra Maria Bossi

Biosensors 2026, 16(1), 12; https://doi.org/10.3390/bios16010012 - 23 Dec 2025

Abstract

In this work, we report the development of a highly sensitive optical sensor for the detection of cardiac troponin I (cTnI), a key biomarker for early-stage myocardial infarction diagnosis. The sensor combines castor oil-derived biomimetic receptors, called GreenNanoMIPs and prepared via the molecular [...] Read more.

In this work, we report the development of a highly sensitive optical sensor for the detection of cardiac troponin I (cTnI), a key biomarker for early-stage myocardial infarction diagnosis. The sensor combines castor oil-derived biomimetic receptors, called GreenNanoMIPs and prepared via the molecular imprinting technology using as a template an epitope of cTnI (i.e., the NR10 peptide), with a portable multimode plastic optical fiber surface plasmon resonance (POF-SPR) transducer. For sensing, gold SPR chips were functionalized with GreenNanoMIPs as proven by refractive index changes and confirmed by means of XPS. Binding experiments demonstrated the cTnI_nanoMIP-SPR sensor’s ability to detect both the NR10 peptide epitope and the full-length cTnI protein within minutes (t = 10 min), with high sensitivity and selectivity in buffer and serum matrices. The cTnI_nanoMIP-SPR showed an LOD of 3.53 × 10⁻¹⁵ M, with a linearity range of 1 pM–100 pM, outperforming previously reported sensor platforms and making it a promising tool for early-stage myocardial infarction detection. Full article

(This article belongs to the Section Optical and Photonic Biosensors)

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5 pages, 154 KB

Open AccessEditorial

Aptamer-Based Biosensors for Point-of-Care Diagnostics

by Ana Díaz-Fernández

Biosensors 2026, 16(1), 11; https://doi.org/10.3390/bios16010011 - 23 Dec 2025

Abstract

Over the past two decades, we have witnessed remarkable progress in the development of biosensors [...] Full article

(This article belongs to the Special Issue Aptamer-Based Biosensors for Point-of-Care Diagnostics)

17 pages, 3144 KB

Open AccessArticle

Integrated Analysis of Behavioral and Physiological Effects of Nano-Sized Carboxylated Polystyrene Particles on Daphnia magna Neonates and Adults: A Video Tracking-Based Improvement of Acute Toxicity Assay

by Silvia Rizzato, Antonella Giacovelli, Gregorio Polo, Fausto Sirsi, Anna Grazia Monteduro, Gayatri Udayan, Muhammad Ahsan Ejaz, Giuseppe Maruccio and Maria Giulia Lionetto

Biosensors 2026, 16(1), 10; https://doi.org/10.3390/bios16010010 - 23 Dec 2025

Abstract

Nanoplastics pose significant environmental and public health risks, prompting the need for sensitive, cost-effective, and rapid assays for ecotoxicity assessment. The present work proposes the use of a portable smartphone-based platform to enhance traditional Daphnia magna acute toxicity assays by integrating behavior analysis [...] Read more.

Nanoplastics pose significant environmental and public health risks, prompting the need for sensitive, cost-effective, and rapid assays for ecotoxicity assessment. The present work proposes the use of a portable smartphone-based platform to enhance traditional Daphnia magna acute toxicity assays by integrating behavior analysis and heart rate measurements. The aim is to improve sensitivity in detecting toxic effects of nanoplastics. In particular, the study focused on nano-sized carboxylated polystyrene (PS) nanoparticles. Two variability factors that could influence biological effects of nanoplastics, the particle size and the age of the organisms, were considered. Results demonstrated that the application of the proposed integrated approach allowed the detection of early subtle effects such as a significant impact on the heart rate and behavior of Daphnia magna under short-term exposure to PS carboxylated nanoparticles. In particular, a stimulation of heart rate was observed for both neonates and adults either for 40 nm or 200 nm particles after 48 h exposure, presumably attributable to an interference of carboxylated PS NPs with adrenergic-type receptors. Behavioral alterations were detectable for 40 nm particles but not for 200 nm ones consisting of a decrease in velocity and alterations of trajectories. Obtained results demonstrated the suitability of the proposed smartphone platform for friendly and real-time integration of behavioral analysis with physiological outcome measurements during acute exposure of Daphnia magna to nano-sized carboxylated PS NPs, expanding the sensitivity of the traditional acute toxicity tests. It offers a novel, cost-effective, and field-applicable method for environmental monitoring of nanoparticle toxicity and impact. Full article

(This article belongs to the Section Environmental Biosensors and Biosensing)

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13 pages, 1966 KB

Open AccessArticle

A Wearable Ultrasound Sensing System for Soft Tissue Stiffness Detection: A Feasibility Study

by Guangshuai Bao, Tongyi Xu, Xiaoyu Li and Bo Meng

Biosensors 2026, 16(1), 9; https://doi.org/10.3390/bios16010009 - 22 Dec 2025

Abstract

Manual palpation serves as a conventional clinical method for assessing soft tissue stiffness; however, its results are susceptible to subjective factors and exhibit limited reliability. To achieve objective evaluation of pathological tissue stiffness, this study utilizes ultrasonic transducers to measure the time-of-flight (ToF) [...] Read more.

Manual palpation serves as a conventional clinical method for assessing soft tissue stiffness; however, its results are susceptible to subjective factors and exhibit limited reliability. To achieve objective evaluation of pathological tissue stiffness, this study utilizes ultrasonic transducers to measure the time-of-flight (ToF) difference in ultrasound signals in silicone samples and ex vivo animal tissues under specific pressure gradients. A correlation model between the ToF difference and tissue stiffness was established, thereby enabling the detection of tissue stiffness. Based on this methodology, a wearable sensing system incorporating ultrasonic transducers was developed. The system applies fixed gradient pressure to human tissues via a pneumatic control unit and detects the corresponding ToF difference, allowing real-time monitoring of stiffness variations in the biceps brachii and thigh during relaxation and contraction, in the forearm during gripping and release actions, as well as in simulated lesions. This study provides a quantitative technological framework for wearable tissue stiffness monitoring, and its objective measurement characteristics offer support for clinical diagnostic decision-making. Full article

(This article belongs to the Special Issue Wearable and Implantable Bioelectronics for Advanced Biosensing and Human Health Monitoring)

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14 pages, 1291 KB

Open AccessArticle

Integrated Microfluidic Giant Magnetoresistance (GMR) Biosensor Platform for Magnetoresistive Immunoassay of Myoglobin

by Yikai Wang, Huaiyu Wang, Yunyun Zhang, Shuhui Cui, Fei Hu and Bo’an Li

Biosensors 2026, 16(1), 8; https://doi.org/10.3390/bios16010008 - 22 Dec 2025

Abstract

Acute myocardial infarction (AMI) is a rapidly progressing cardiovascular condition associated with high mortality. Myoglobin is an early biomarker of AMI; however, its detection using conventional methods is limited by complex workflows and low resistance to interference. In this study, we developed an [...] Read more.

Acute myocardial infarction (AMI) is a rapidly progressing cardiovascular condition associated with high mortality. Myoglobin is an early biomarker of AMI; however, its detection using conventional methods is limited by complex workflows and low resistance to interference. In this study, we developed an integrated myoglobin detection platform that combined magneto-immunoassay with microfluidic technology. A giant magnetoresistance (GMR) sensor was fabricated using micro-electro-mechanical systems (MEMS). The designed microfluidic chip integrated sample pretreatment, immunoreaction, and magnetic signal capture functionalities. Its built-in GMR sensor, labeled with magnetic nanoparticles, directly converted the “antigen–antibody” biochemical signal into detectable magnetoresistance changes, thereby enabling the indirect detection of myoglobin. A magneto-immunoassay analysis system consisted of a fluidic drive, magnetic field control, and data acquisition functions. Various key parameters were optimized, including EDC/NHS concentration, antibody concentration, and magnetic bead size. Under the optimal conditions and using 1 μm magnetic beads as labels and an external detection magnetic field of 60 Oe, the platform successfully detected myoglobin at 75 ng/mL with ∆MR ≥ 0.202%. Specificity tests demonstrated that the platform had high specificity for myoglobin and could effectively distinguish myoglobin from bovine serum albumin (BSA) and troponin I. This study presents a rapid, accurate myoglobin detection platform that can be applied for the early diagnosis of AMI. Full article

(This article belongs to the Special Issue Biosensing Technologies in Medical Diagnosis—2nd Edition)

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29 pages, 1649 KB

Open AccessReview

Polymer-Based Gas Sensors for Detection of Disease Biomarkers in Exhaled Breath

by Guangjie Shao, Yanjie Wang, Zhiqiang Lan, Jie Wang, Jian He, Xiujian Chou, Kun Zhu and Yong Zhou

Biosensors 2026, 16(1), 7; https://doi.org/10.3390/bios16010007 - 22 Dec 2025

Abstract

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, [...] Read more.

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, excellent flexibility, and tunable chemical properties. This review comprehensively summarized recent advancements in polymer-based gas sensors for the detection of disease biomarkers in exhaled breath. The gas-sensing mechanism of polymers, along with novel gas-sensitive materials such as conductive polymers, polymer composites, and functionalized polymers was examined in detail. Moreover, key applications in diagnosing diseases, including asthma, chronic kidney disease, lung cancer, and diabetes, were highlighted through detecting specific biomarkers. Furthermore, current challenges related to sensor selectivity, stability, and interference from environmental humidity were discussed, and potential solutions were proposed. Future perspectives were offered on the development of next-generation polymer-based sensors, including the integration of machine learning for data analysis and the design of electronic-nose (e-nose) sensor arrays. Full article

(This article belongs to the Special Issue Polymers-Based Biosensors and Bioelectronics: Designs and Applications)

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31 pages, 25892 KB

Open AccessReview

Nanoaggregate-Based Innovative Electrochemiluminescence Sensors for Foodborne Contaminant Analysis

by Tingting Han, Jinyang Zhuang, Yueling Lu, Jianhong Xu and Jun-Jie Zhu

Biosensors 2026, 16(1), 6; https://doi.org/10.3390/bios16010006 - 22 Dec 2025

Abstract

The pervasive presence of foodborne contaminants in foods poses a significant global threat, contributing to various foodborne diseases and food safety issues. Therefore, developing rapid, sensitive, and universal detection methods for them is essential to ensure public health and food safety. Electrochemiluminescence (ECL) [...] Read more.

The pervasive presence of foodborne contaminants in foods poses a significant global threat, contributing to various foodborne diseases and food safety issues. Therefore, developing rapid, sensitive, and universal detection methods for them is essential to ensure public health and food safety. Electrochemiluminescence (ECL) sensors, particularly those incorporating innovative nanoaggregates, have been widely used to detect related contaminant residues in foodstuffs owing to their superior sensitivity and low background signals. This review summarizes recent advances in nanoaggregate-based novel ECL sensors for detecting a wide range of contaminants, with emphasis on their fundamentals and representative applications. This area has not yet been comprehensively covered in the existing literature. The current challenges and emerging trends for next-generation ECL sensors based on nanoaggregates in food safety monitoring are also discussed. Full article

(This article belongs to the Special Issue Biosensors for Environmental Monitoring and Food Safety)

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19 pages, 4208 KB

Open AccessArticle

Two-in-One Hybrid Sensor Based on PV4D4/AgAu/TiO₂ Structure for Carbon Dioxide and Hydrogen Gas Detection in Biomedical and Industrial Fields

by Mihai Brinza, Lynn Schwäke, Stefan Schröder, Cristian Lupan, Nicolai Ababii, Nicolae Magariu, Maxim Chiriac, Franz Faupel, Alexander Vahl and Oleg Lupan

Biosensors 2026, 16(1), 5; https://doi.org/10.3390/bios16010005 - 22 Dec 2025

Abstract

A novel two-in-one sensor for both carbon dioxide and hydrogen detection has been obtained based on a hybrid heterostructure. It consists of a 30 nm thick TiO₂ nanocrystalline film grown by atomic layer deposition (ALD), thermally annealed at 610 °C, and subsequently [...] Read more.

A novel two-in-one sensor for both carbon dioxide and hydrogen detection has been obtained based on a hybrid heterostructure. It consists of a 30 nm thick TiO₂ nanocrystalline film grown by atomic layer deposition (ALD), thermally annealed at 610 °C, and subsequently coated with bimetallic AgAu nanoparticles and covered with a PV4D4 nanolayer, which was thermally treated at 430 °C. Two types of gas response behaviors have been registered, as n-type for hydrogen gas and p-type semiconductor behavior for carbon dioxide gas detection. The highest response for carbon dioxide has been registered at an operating temperature of 150 °C with a value of 130%, while the highest response for hydrogen gas was registered at 350 °C with a value of 230%, although it also attained a relatively good gas selectivity at 150 °C. It is considered that a thermal annealing temperature of 610 °C is better for the properties of TiO₂ nanofilms, since it enhances gas sensor sensitivity too. Polymer coating on top is also believed to contribute to a higher influence on selectivity of the sensor structure. Accordingly, to our previous research where PV4D4 has been annealed at 450 °C, in this research paper, a lower temperature of 430 °C for annealing has been used, and thus another ratio of cyclocages and cyclorings has been obtained. Knowing that the polymer acts like a sieve atop the sensor structure, in this study it offers increased selectivity and sensitivity towards carbon dioxide gas detection, as well as maintaining a relatively increased selectivity for hydrogen gas detection, which works as expected with Ag and Au bimetallic nanoparticles on the surface of the sensing structure. The results obtained are highly important for biomedical and environmental applications, as well as for further development of the sensor industry, considering the high potential of two-in-one sensors. A carbon dioxide detector could be used for assessing respiratory markers in patients and monitoring the quality of the environment, while hydrogen could be used for both monitoring lactose intolerance and concentrations in cases of therapeutic gas, as well as monitoring the safe handling of various concentrations. Full article

(This article belongs to the Section Biosensor Materials)

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24 pages, 2834 KB

Open AccessReview

Biosensors for Detection of Labile Heme in Biological Samples

by Krysta Dobill, Delphine Lechardeur and Jasmina Vidic

Biosensors 2026, 16(1), 4; https://doi.org/10.3390/bios16010004 - 19 Dec 2025

Abstract

Heme, a protoporphyrin IX iron complex, functions as an essential prosthetic group in hemoglobin and myoglobin, mediating oxygen storage and transport. Additionally, heme serves as a critical cofactor in various enzymes such as cytochrome c, enabling electron transfer within the mitochondrial respiratory chain. [...] Read more.

Heme, a protoporphyrin IX iron complex, functions as an essential prosthetic group in hemoglobin and myoglobin, mediating oxygen storage and transport. Additionally, heme serves as a critical cofactor in various enzymes such as cytochrome c, enabling electron transfer within the mitochondrial respiratory chain. Unlike protein-bound heme, free or labile heme exhibits cytotoxic, pro-oxidant, and pro-inflammatory properties. Elevated levels of free heme are associated with various pathophysiological conditions, including hemolytic disorders such as sickle cell disease, malaria, and sepsis. In this review, we introduce the physiological roles of heme and its involvement in human health and disease. We also examine the mechanisms of heme sensing and regulation in bacterial cells. A variety of analytical methods have been developed to detect and quantify heme, enabling differentiation between protein-bound and free forms. These tools are discussed in the context of their applications in studying cellular heme regulation and their use in monitoring pathological conditions in humans. In particular, we describe examples of biosensors employing bacterial heme sensor proteins as recognition elements. Full article

(This article belongs to the Special Issue Microbial Biosensor: From Design to Applications—2nd Edition)

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25 pages, 3630 KB

Open AccessArticle

When Droplets Can “Think”: Intelligent Testing in Digital Microfluidic Chips

by Zhijie Luo, Shaoxin Li, Wufa Long, Rui Chen and Jianhua Zheng

Biosensors 2026, 16(1), 3; https://doi.org/10.3390/bios16010003 - 19 Dec 2025

Abstract

Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This [...] Read more.

Digital microfluidic biochips (DMFBs) find extensive applications in biochemical experiments, medical diagnostics, and safety-critical domains, with their reliability dependent on efficient online testing technologies. However, traditional random search algorithms suffer from slow convergence and susceptibility to local optima under complex fluidic constraints. This paper proposes a hybrid optimization method based on priority strategy and an improved sparrow search algorithm for DMFB online test path planning. At the algorithmic level, the improved sparrow search algorithm incorporates three main components: tent chaotic mapping for population initialization, cosine adaptive weights together with Elite Opposition-based Learning (EOBL) to balance global exploration and local exploitation, and a Gaussian perturbation mechanism for fine-grained refinement of promising solutions. Concurrently, this paper proposes an intelligent rescue strategy that integrates global graph-theoretic pathfinding, local greedy heuristics, and space–time constraint verification to establish a closed-loop decision-making system. The experimental results show that the proposed algorithm is efficient. On the standard 7 × 7–15 × 15 DMFB benchmark chips, the shortest offline test path length obtained by the algorithm is equal to the length of the Euler path, indicating that, for these regular layouts, the shortest test path has reached the known optimal value. In both offline and online testing, the shortest paths found by the proposed method are better than or equal to those of existing mainstream algorithms. In particular, for the 15 × 15 chip under online testing, the proposed method reduces the path length from 543 and 471 to 446 compared with the IPSO and IACA algorithms, respectively, and reduces the standard deviation by 53.14% and 39.4% compared with IGWO in offline and online testing. Full article

(This article belongs to the Special Issue Intelligent Microfluidic Biosensing)

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