Biosensors

28 pages, 14925 KB

Open AccessArticle

State-Referenced Truncated SVD for Dynamic Microwave Monitoring of Intracranial Hemorrhage

by Zekun Zhang, Heng Liu, Ruide Li, Huiyuan Zhu, Fan Li, Shujun Ni, Aojun Liu and Yao Zhai

Biosensors 2026, 16(5), 285; https://doi.org/10.3390/bios16050285 - 14 May 2026

Abstract

Microwave imaging is a promising non-ionizing technique for bedside follow-up of intracranial hemorrhage, but dynamic monitoring remains challenging under limited multistatic sampling because weak inter-frame changes can be obscured by measurement variability, model mismatch, and the high cost of frame-by-frame nonlinear inversion. To [...] Read more.

Microwave imaging is a promising non-ionizing technique for bedside follow-up of intracranial hemorrhage, but dynamic monitoring remains challenging under limited multistatic sampling because weak inter-frame changes can be obscured by measurement variability, model mismatch, and the high cost of frame-by-frame nonlinear inversion. To address this problem, this paper proposes a state-referenced truncated singular-value decomposition (SR-TSVD) framework for dynamic microwave monitoring of hemorrhagic evolution. The method maintains an internal gate state and reconstructs only the state-referenced increment at each monitoring instant. A row-whitened TSVD inversion is introduced to reduce channel dominance effects and improve robustness to route-dependent imbalance, while a residual-driven gate-refresh mechanism updates the internal state only when the current linearization background becomes insufficiently accurate. The proposed method was validated through two-dimensional numerical experiments and hardware phantom measurements. The numerical study examined different lesion evolution scenarios and analyzed the effects of antenna count, frequency diversity, and measurement noise. The hardware study showed that the method preserves the main dynamic evolution in a real measurement system and remains more stable than baseline linear methods under sparse array conditions. These results indicate that SR-TSVD provides an effective and computationally practical framework for repeated bedside microwave monitoring of intracranial hemorrhage. Full article

(This article belongs to the Special Issue Biosensors for Physiological Signal Monitoring)

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20 pages, 8407 KB

Open AccessArticle

Spatially Resolved Biosensing of Localized Dopamine Release via Its Electropolymerization Using Plasmonic Electrochemical Microscopy

by Christian Martinez, Samuel Groysman, Madison Ngo and Yixian Wang

Biosensors 2026, 16(5), 284; https://doi.org/10.3390/bios16050284 - 14 May 2026

Abstract

The precise spatiotemporal monitoring of dopamine is critical for understanding neurotransmission and neurodegenerative pathologies. While traditional electrochemical methods offer excellent temporal resolution, they lack the spatial resolution required to map network-wide dynamic events. To address this, we adapted a wide-field plasmonic electrochemical microscopy [...] Read more.

The precise spatiotemporal monitoring of dopamine is critical for understanding neurotransmission and neurodegenerative pathologies. While traditional electrochemical methods offer excellent temporal resolution, they lack the spatial resolution required to map network-wide dynamic events. To address this, we adapted a wide-field plasmonic electrochemical microscopy (PEM) platform to spatially image localized electrochemical reactions. Specifically, we leveraged the anodic electropolymerization of dopamine into a surface-confined polydopamine nanofilm to enable label-free, pixel-level optical quantification. Bulk solution testing demonstrated highly uniform sensor sensitivity, yielding an estimated single-pixel limit of detection of 14 pM. Furthermore, utilizing a custom injection system, we successfully imaged the real-time localized delivery of micromolar dopamine concentrations and demonstrated qualitative responsiveness of the integrated optical signal to delivered dopamine as a proof-of-concept for the platform. The platform functions as a spatially resolved mass integrator while simultaneously decoupling this chemical signal from transient hydrodynamic mechanical deformations caused by dopamine injection flow. Ultimately, this platform establishes the fundamental methodology required for future high-throughput spatial monitoring of complex neurotransmitter release dynamics across cellular networks. Full article

(This article belongs to the Special Issue Functional Nanomaterials for Advanced Biosensing: From Molecular Design to Real-World Applications)

21 pages, 1467 KB

Open AccessReview

State-of-the-Art and Next Generation Intra-Articular Implantable Biosensors for Osteoarthritis: From Analytical Limits to Operational Stability

by Abdullateef Gbolahan Olayiwola, Albina Abdossova, Daniele Tosi, Gorka Orive, Zhe Liu and Cevat Erisken

Biosensors 2026, 16(5), 283; https://doi.org/10.3390/bios16050283 - 14 May 2026

Abstract

Osteoarthritis (OA) and osteochondral degeneration present a significant clinical burden characterized by the complex interplay of extracellular matrix degradation and chronic inflammation. While biochemical profiling has matured, a critical translational gap remains in transitioning from benchtop assays to systems capable of continuous, intra-articular [...] Read more.

Osteoarthritis (OA) and osteochondral degeneration present a significant clinical burden characterized by the complex interplay of extracellular matrix degradation and chronic inflammation. While biochemical profiling has matured, a critical translational gap remains in transitioning from benchtop assays to systems capable of continuous, intra-articular monitoring. This review provides a comprehensive synthesis of experimentally validated biosensing technologies, including optical, electrochemical, and piezoelectric Quartz Crystal Microbalance (QCM) platforms, evaluated through the lens of sensing architecture, biomarker specificity, and matrix compatibility. Our analysis reveals that while optical sensors offer superior sensitivity, electrochemical platforms show the greatest promise for miniaturized, implantable integration. However, a pivot in the field is identified: the primary bottleneck has shifted from analytical detection limits to operational stability within the hostile synovial environment. Current research is largely restricted to single-analyte detection in simplified media, failing to address the multifactorial nature of OA. We propose that the next generation of osteochondral diagnostics must prioritize multiplexed arrays, mechanically compliant architectures, and machine-learning-assisted signal processing. By bridging these engineering frontiers, biosensors will evolve from passive diagnostic tools into intelligent, personalized platforms for real-time disease management. Full article

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

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12 pages, 2553 KB

Open AccessArticle

Label-Free Quantification of Bilirubin Using a Refractive Index-Insensitive Nanolaminate SERS Substrate

by Jiwon Yun, Inyoung Kim and Wonil Nam

Biosensors 2026, 16(5), 282; https://doi.org/10.3390/bios16050282 - 14 May 2026

Abstract

Bilirubin is an important biomarker, where a small unbound fraction dissociated from albumin can cross the blood–brain barrier and induce neurotoxicity, such as kernicterus, at low nanomolar levels. Accurate detection of this low-level fraction remains challenging. Surface-enhanced Raman spectroscopy (SERS) enables label-free molecular [...] Read more.

Bilirubin is an important biomarker, where a small unbound fraction dissociated from albumin can cross the blood–brain barrier and induce neurotoxicity, such as kernicterus, at low nanomolar levels. Accurate detection of this low-level fraction remains challenging. Surface-enhanced Raman spectroscopy (SERS) enables label-free molecular detection; however, variations in the local refractive index (RI) at plasmonic hotspots can detune the resonance from the excitation wavelength, leading to signal fluctuations and limited quantitative reliability. Here, we present a multi-resonant nanolaminate SERS substrate designed to achieve RI-insensitive and robust signal enhancement. The vertically stacked metal–insulator–metal architecture provides broadband spectral overlap with both excitation and Raman scattering under dielectric loading, maintaining consistent enhancement across varying RI conditions. We demonstrate label-free bilirubin detection with a highly linear response over 10⁻⁹ to 10⁻⁴ M, achieving an R² value of 0.99. Compared with previously reported bilirubin SERS substrates relying mainly on single-resonant plasmonic enhancement, this RI-insensitive design offers improved quantitative reliability under dielectric environmental changes. These results highlight the importance of RI-insensitive SERS design for reliable quantification and provide a general strategy for robust SERS-based biosensing. Full article

(This article belongs to the Special Issue Surface-Enhanced Raman Scattering in Biosensing Applications)

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30 pages, 1002 KB

Open AccessReview

Microfluidic and MEMS-Based Biosensing Platforms for Fungal Respiratory Infections in Immunocompromised Patients: Toward Rapid, Specific, and Minimally Invasive Diagnosis

by Vasiliki E. Georgakopoulou and Vassiliki C. Pitiriga

Biosensors 2026, 16(5), 281; https://doi.org/10.3390/bios16050281 - 12 May 2026

Abstract

Invasive fungal respiratory infections (IFRIs) remain a major cause of morbidity and mortality among immunocompromised patients, yet diagnosis continues to be hindered by nonspecific clinical features, limited sample accessibility, and the poor sensitivity or specificity of conventional tests. Microfluidic and microelectromechanical systems (MEMS)-based [...] Read more.

Invasive fungal respiratory infections (IFRIs) remain a major cause of morbidity and mortality among immunocompromised patients, yet diagnosis continues to be hindered by nonspecific clinical features, limited sample accessibility, and the poor sensitivity or specificity of conventional tests. Microfluidic and microelectromechanical systems (MEMS)-based biosensing platforms have emerged as promising alternatives, enabling rapid, minimally invasive, and highly specific detection of fungal pathogens and host responses. Microfluidic nucleic acid and antigen assays allow on-chip amplification and immunodetection with reduced sample volumes and turnaround times, while CRISPR-enhanced systems further improve analytical sensitivity. Parallel advances in host response profiling—including transcriptomic, proteomic, and cytokine-based signatures—have demonstrated feasibility for integration into lab-on-a-chip platforms. MEMS-based technologies extend this potential by facilitating real-time analysis of exhaled volatile organic compounds, mechanical biosensing of fungal DNA and antigens, and in situ monitoring of device-associated biofilms. Translational studies highlight potential applications across intensive care, hematology–oncology, and transplant settings, as well as in outpatient monitoring of high-risk populations. However, several challenges remain, including limited multicenter validation, matrix-related biofouling effects, and a lack of standardization in fungal biomarker panels. Future directions include AI-driven interpretation of multianalyte data, multiplexed integration of host and pathogen markers, and development of fully cartridge-based systems for near-patient deployment. Collectively, these innovations may shift fungal diagnostics toward earlier, more precise, and patient-tailored interventions, improving outcomes in vulnerable populations. Full article

(This article belongs to the Special Issue Advanced Microfluidic Devices and MEMS in Biosensing Applications)

27 pages, 4867 KB

Open AccessReview

Insights into Sensing and Biomedical Domains Using Multi-Synthetic Covalent Organic Frameworks

by Hassan Imam Rizvi, Yuchen Qiao, Shilpa Dabas, Peng Ren and Xuemei Yang

Biosensors 2026, 16(5), 280; https://doi.org/10.3390/bios16050280 - 11 May 2026

Abstract

Covalent organic frameworks (COFs) are one of the most important crystalline structures, having high porosity, and are mostly composed of lighter elements, such as H, C, N, O, etc., with covalent bonds between them. They are chemically synthesized in a repetitive arrangement and [...] Read more.

Covalent organic frameworks (COFs) are one of the most important crystalline structures, having high porosity, and are mostly composed of lighter elements, such as H, C, N, O, etc., with covalent bonds between them. They are chemically synthesized in a repetitive arrangement and create a highly effective porous surface area that plays a fundamental role in various applications including sensing and biomedical applications. This study offers an overview of COFs in sensing and biomedical applications and provides a detailed overview of various synthesis procedures of COFs. Next, we explore their innovative sensing performances in the cases of various gases, ions and metals. Finally, it is emphasized that the major biomedical applications of COFs have been addressed regarding diseases and treatment strategies. Overall, this review offers an overview of COFs’ capabilities and promising behaviors in enhancing and revolutionizing sensing and biomedical technologies. Full article

(This article belongs to the Special Issue Advances in Biosensors Based on Framework Materials)

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13 pages, 6932 KB

Open AccessArticle

DNA Electrochemical Sensor Based on Exonuclease III-Assisted Cycling Signal Amplification for Ultrasensitive Detection of Genetically Modified Soybean

by Lidan Niu, Siyu Huang, Jinmei Zhao, Wenjing Yang, Zhengnan Li, Siqi Niu, Jianchun Yang, Shiqi Chen and Qihui Wang

Biosensors 2026, 16(5), 279; https://doi.org/10.3390/bios16050279 - 11 May 2026

Abstract

The safety of genetically modified crops, particularly the commercial cultivation of glyphosate-resistant genetically modified soybeans, has given rise to significant public concern. Consequently, there is an urgent need to develop efficient and precise methods for detecting genetically modified components. The present study constructed [...] Read more.

The safety of genetically modified crops, particularly the commercial cultivation of glyphosate-resistant genetically modified soybeans, has given rise to significant public concern. Consequently, there is an urgent need to develop efficient and precise methods for detecting genetically modified components. The present study constructed a novel electrochemical biosensor based on nucleic acid exonuclease III (Exo III)-assisted cyclic signal amplification and hairpin probe recognition for the highly sensitive and specific detection of the CP4-EPSPS gene in genetically modified soybeans. The sensor achieves exponential signal amplification by triggering Exo III to cyclically cleave the hairpin probe (H1) upon target DNA binding. Subsequent to this, the released DNA fragments hybridize with the methylene blue-labeled signal probe (HS-MB) that has been immobilized on the electrode surface. This process induces conformational changes and a decrease in the current signal, thereby enabling quantitative analysis of the target gene. The experimental phase of the study successfully validated the sensor’s mechanism and systematically optimized key parameters such as Exo III concentration and reaction time. In optimal conditions, the sensor demonstrated excellent linearity with target DNA concentrations ranging from 100 fM to 10 nM, achieving a detection limit as low as 0.1072 pM. Furthermore, it exhibited remarkable repeatability and stability. This study provides an analytical tool with broad application prospects for the rapid and precise detection of genetically modified crops. Full article

(This article belongs to the Special Issue Emerging Materials for Biosensing in Nano/Microfluidics)

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17 pages, 2237 KB

Open AccessArticle

Systematic Benchmarking of Spectral Demodulation Methods for Ball Resonator and Hybrid FPI–Ball Resonator Sensors for Multiparameter Physiological Monitoring

by Natsnet Bereket Tecle and M. Fátima Domingues

Biosensors 2026, 16(5), 278; https://doi.org/10.3390/bios16050278 - 11 May 2026

Abstract

Ball resonator optical fiber sensors (OFSs) can offer multiparameter sensing capability, but their non-periodic and low-finesse reflection spectra make conventional spectral demodulation unreliable. This work proposes two sensor configurations: (i) a ball resonator and (ii) a hybrid sensor integrating a Fabry–Pérot interferometer (FPI) [...] Read more.

Ball resonator optical fiber sensors (OFSs) can offer multiparameter sensing capability, but their non-periodic and low-finesse reflection spectra make conventional spectral demodulation unreliable. This work proposes two sensor configurations: (i) a ball resonator and (ii) a hybrid sensor integrating a Fabry–Pérot interferometer (FPI) with a ball resonator, and compares their performance for multiparameter physiological monitoring using the Karhunen–Loève transform (KLT). The sensors were evaluated for glucose concentration (0–3 mg/mL), temperature (20–55 °C), and pH (3–9) monitoring. The ball resonator sensor, paired with KLT, achieved high linearity across all measurands (R² = 0.989, 0.919, and 0.838 in response to glucose, temperature, and pH, respectively). The hybrid sensor exhibited a higher glucose sensitivity (6.15 a.u./(mg/mL)) compared to the ball resonator (3.77 a.u./(mg/mL)), resulting in limits of detection (LODs) of 2.53 mM and 4.19 mM, respectively. In contrast, the ball resonator sensor demonstrated better sensitivity for temperature and pH sensing. Furthermore, we present a comprehensive benchmarking framework of seven spectral demodulation methods for OFSs. The results demonstrated that KLT consistently provides robust demodulation performance and highlighted the potential of KLT for multiparameter physiological sensing applications. Full article

(This article belongs to the Special Issue Photonics for Bioapplications: Sensors and Technology—2nd Edition)

20 pages, 3620 KB

Open AccessArticle

Laser-Scribed Graphene on PDMS for Flexible Wearable Sweat Biosensors with Multiplexed Sensing Capability

by Aida Rakhimbekova, Lavita Nuraviana Rizalputri, Aris Konstantinidis, Saptami Suresh Shetty and Khaled Nabil Salama

Biosensors 2026, 16(5), 277; https://doi.org/10.3390/bios16050277 - 11 May 2026

Abstract

Sweat is a valuable biofluid for non-invasive health monitoring, as it contains electrolytes, metabolites, and organic compounds that can correlate with blood levels, making it highly attractive for wearable sensing. Building on advances in low-cost, portable electrochemical sensors, sweat analysis enables tracking of [...] Read more.

Sweat is a valuable biofluid for non-invasive health monitoring, as it contains electrolytes, metabolites, and organic compounds that can correlate with blood levels, making it highly attractive for wearable sensing. Building on advances in low-cost, portable electrochemical sensors, sweat analysis enables tracking of hydration status, metabolic stress, and energy availability via key markers such as sodium, potassium, lactate, and glucose. In the sports context, such wearable platforms can support performance optimization and recovery by assessing fluid loss and electrolyte balance in real time. Here, a multiplexed wearable sweat patch is developed to simultaneously monitor temperature, pH, ammonium, sodium, and sweat rate. The integrated platform demonstrates sensitivities of 10.1 mV/ln[NH₄⁺], 9.1 mV/ln[K⁺], 1.11 mV/ln[Na⁺], 14 mV/pH, 0.19% °C⁻¹, and approximately −1.0 mA (mL/min)⁻¹ for sweat rate, with stable signals and linear calibration responses over relevant physiological ranges. The sensor is implemented on a lightweight, biocompatible laser-scribed graphene on a PDMS substrate suitable for prolonged skin contact and mechanical deformation. In addition, a custom PDMS adhesive patch with optimized suction-cup microstructures is engineered to improve skin adhesion under both dry and wet conditions. Finally, the design of the platform was inspired by an adaptive cycling marathon across Saudi Arabia, where an earlier prototype of a wearable patch was deployed for real-time monitoring during a 30-day campaign. Full article

(This article belongs to the Special Issue Wearable Sensors and Biosensors for Physiological Signals Measurement)

17 pages, 3533 KB

Open AccessArticle

A Comparison of Cost-Effective Sensors for a Fluorescence-Based Detection System Used in Biodiagnostic Devices

by Rebecca Vornweg, Christian Decool, Moritz Hemmer, Bastian Stollfuss, Thomas Guenther, Ann-Kathrin Löffler, Roland E. Kontermann and André Zimmermann

Biosensors 2026, 16(5), 276; https://doi.org/10.3390/bios16050276 - 11 May 2026

Abstract

Cost-effective, fluorescence-based biodiagnostic devices have the potential to expand point-of-care (POC) testing in resource-limited settings, thereby creating new opportunities for accessible and decentralised diagnostics. The objective of this study is to investigate the performance of a newly designed and simplified system that uses [...] Read more.

Cost-effective, fluorescence-based biodiagnostic devices have the potential to expand point-of-care (POC) testing in resource-limited settings, thereby creating new opportunities for accessible and decentralised diagnostics. The objective of this study is to investigate the performance of a newly designed and simplified system that uses several cost-effective sensors for use in a fluorescence-based biodiagnostic setup. A collecting setup that includes an elliptical mirror to focus emitted fluorescence onto the semiconductor sensors was designed for an intensity-based readout. The readout was realised by various photodiodes, photoresistors, phototransistors and one multi-pixel photon counter (MPPC). Over a range of fluorophore concentrations using serial dilutions of Rhodamine B (RhB), the limit of detection (LOD) and limit of quantification (LOQ) at system level were evaluated. With the exception of the photodiodes, the system demonstrated promising performance with all sensors. The system using the photoresistors achieved the lowest LOD but showed limited repeatability, while the system using the MPPC and phototransistors exhibited high repeatability. A proof-of-concept demonstrated the feasibility of a photoresistor-based configuration by using a sandwich immunoassay for the detection of the antigen Human Epidermal growth factor Receptor 2 (HER2) (100 nM). While this study has not yet encompassed the full spectrum of clinically relevant concentrations, it has yielded valuable insights to enable further enhancements, without the necessity for highly specialised or costly equipment. The limitations and necessary improvements for further developments are discussed. Full article

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

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27 pages, 3921 KB

Open AccessArticle

RT-AFNet: A Hybrid ResNet-Transformer Architecture with Multi-Scale Fusion for Atrial Fibrillation Detection

by Xinyu Hu, Qingqing Duan, Yuwei Zhang, Caiyun Ma, Chang Yan and Chengyu Liu

Biosensors 2026, 16(5), 275; https://doi.org/10.3390/bios16050275 - 9 May 2026

Abstract

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with an elevated risk of severe complications, including stroke and heart failure. Due to its paroxysmal nature and the inherent complexity of electrocardiogram (ECG) signals, developing highly accurate and robust automated detection methods remains [...] Read more.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with an elevated risk of severe complications, including stroke and heart failure. Due to its paroxysmal nature and the inherent complexity of electrocardiogram (ECG) signals, developing highly accurate and robust automated detection methods remains a critical challenge. To address the limitations of existing models in simultaneously capturing local morphological anomalies and long-range temporal dependencies, we proposed RT-AFNet, a novel hybrid ResNet-Transformer architecture. Specifically, RT-AFNet integrated the robust local feature extraction capabilities of a Residual Neural Network (ResNet) backbone with the global temporal modeling power of a lightweight self-attention mechanism. Furthermore, a multi-scale feature fusion strategy was introduced to optimize feature representation. The proposed RT-AFNet model was evaluated on three public AF databases: the China Physiological Signal Challenge 2018 (CPSC2018), the PhysioNet/Computing in Cardiology Challenge 2017 (CinC2017), and the MIT-BIH Atrial Fibrillation Database (MIT-BIH AF). The proposed model achieved F1 scores of 99.76%, 97.47%, and 96.20%, along with area under the curve (AUC) values of 99.97%, 98.98%, and 98.28% on the three datasets, respectively. These results demonstrate that the proposed architecture exhibits excellent generalization ability and stability across different databases, providing a robust and reliable deep learning solution for automated AF screening. Full article

(This article belongs to the Special Issue Biosensors for Disease Analysis)

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14 pages, 1201 KB

Open AccessArticle

Ultrasensitive Label-Free Detection of Free Thyroxine (T4) in Physiological Ranges Using Aptamer-Functionalized Silicon Nanowire Field Effect Transistors

by Stephanie Klinghammer, Wiana Butko, Alexandra Parichenko, Gylxhane Kastrati, Abdallh Herbawi, Leif Riemenschneider and Gianaurelio Cuniberti

Biosensors 2026, 16(5), 274; https://doi.org/10.3390/bios16050274 - 9 May 2026

Abstract

Thyroxine (T4) is a key hormone regulating metabolic, cardiovascular, and neurodevelopmental processes, yet its clinical quantification still relies on centralized immunoassays that limit rapid or point-of-care monitoring. Here, we present a label-free biosensing platform based on silicon nanowire field-effect transistors (SiNW-FETs) functionalized with [...] Read more.

Thyroxine (T4) is a key hormone regulating metabolic, cardiovascular, and neurodevelopmental processes, yet its clinical quantification still relies on centralized immunoassays that limit rapid or point-of-care monitoring. Here, we present a label-free biosensing platform based on silicon nanowire field-effect transistors (SiNW-FETs) functionalized with a T4-selective DNA aptamer via a 3-Triethoxysilyl propylsuccinic Anhydride (TESPSA)-mediated silanization approach, enabling a streamlined two-step modification for oriented immobilization. The biosensor achieves robust real-time detection of T4 across the physiological concentration range (5–30 pM), with a limit of detection of ~5 pM and a strong linear correlation between drain current and analyte concentration (R² = 0.9931). Specificity is confirmed using non-functionalized devices and estradiol as a non-target control. All measurements were performed in undiluted phosphate-buffered saline, representing a physiologically relevant ionic environment and demonstrating stable sensor performance under realistic buffer conditions. The dose–response behavior follows a Hill model, allowing extraction of binding parameters and confirming that the electrical signal originates from specific aptamer–target interactions. These results demonstrate that aptamer-functionalized SiNW-FETs provide a highly sensitive, selective, and miniaturizable platform for quantitative thyroid hormone monitoring, with strong potential for future point-of-care applications. Full article

(This article belongs to the Special Issue Multidimensional Nanomaterial-Based Biosensors for Environmental and Healthcare Monitoring)

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16 pages, 14520 KB

Open AccessArticle

Tunable Particle Separation in a Straight Microchannel via Symmetrical Viscoelastic Sheath Flows

by Tianyuan Zhou, Qi Cui, Guizhong Tian, Jing Xia, Ping Liu, Yoichiroh Hosokawa, Yaxiaer Yalikun, Pan Wang, Shilun Feng and Tianlong Zhang

Biosensors 2026, 16(5), 273; https://doi.org/10.3390/bios16050273 - 8 May 2026

Abstract

In this study, we present a novel microfluidic platform for tunable size-based particle separation within a straight microchannel using symmetrical viscoelastic sheath flows. The device incorporates two pairs of symmetrical microchannels for sheath fluid injection: the first pair facilitates particle focusing and separation, [...] Read more.

In this study, we present a novel microfluidic platform for tunable size-based particle separation within a straight microchannel using symmetrical viscoelastic sheath flows. The device incorporates two pairs of symmetrical microchannels for sheath fluid injection: the first pair facilitates particle focusing and separation, while the second pair enables dynamic regulation of the separation distance between particle streams. Experimental results demonstrate that a 50 ppm polyethylene oxide (PEO) solution focuses 1 μm polystyrene particles toward the channel centerline via elastic forces, whereas 5 μm particles migrate toward the channel sidewalls under dominant inertial forces, effectively overcoming the elastic effects. The interplay between inertial and elastic forces thus achieves size-dependent particle separation. Furthermore, by adjusting the flow rate of the PEO sheath in the second pair of microchannels, the separation distance between the two particle populations can be modulated in real time. Higher PEO concentrations (500 and 1000 ppm) exhibit enhanced capabilities to deflect particle flow streams. By contrast, the lower PEO concentrations like 50, 100 and 200 ppm are more versatile in adjusting the separation distance. The biological applicability of this platform is further demonstrated through the tunable separation of Escherichia coli (E. coli) and Chlorella vulgaris (C. vulgaris). This microfluidic device demonstrates significant potential for downstream particle processing applications, including real-time particle detection and targeted drug delivery. Full article

(This article belongs to the Special Issue Design and Application of Microfluidic Biosensors in Biomedicine—2nd Edition)

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20 pages, 3228 KB

Open AccessArticle

Low-Effort Respiratory Function Estimation with a Soft Wearable Digital Spirometry Patch

by Faheem A. Karim, Ahmed Tariq, Christopher B. Fitzpatrick, Lauren Zhou, Mayte Suárez-Fariñas, Helena Schotland, Linda Rogers, Yoon Jae Lee, Woon-Hong Yeo and Yun Soung Kim

Biosensors 2026, 16(5), 272; https://doi.org/10.3390/bios16050272 - 8 May 2026

Abstract

Spirometry is widely regarded as the clinical gold standard for quantifying lung function. It plays a central role in the diagnosis and management of cardiopulmonary disorders, including asthma and chronic obstructive pulmonary disease (COPD). However, the procedure relies on a forceful and often [...] Read more.

Spirometry is widely regarded as the clinical gold standard for quantifying lung function. It plays a central role in the diagnosis and management of cardiopulmonary disorders, including asthma and chronic obstructive pulmonary disease (COPD). However, the procedure relies on a forceful and often stressful expiratory maneuver that may cause patient discomfort and require substantial effort, frequently necessitating active coaching and trained personnel to ensure reproducible measurements. In this paper, we present the Digital Spirometry Patch (DSP), a soft, flexible, wearable patch capable of estimating lung function parameters by utilizing low-effort breathing maneuvers. Eighteen participants performed low-effort and forceful breathing maneuvers while wearing the DSP to collect tracheal sound and chest movement signals for spirometric parameter estimation using elastic net and simple linear regression. Using leave-one-subject-out cross-validation, the elastic net models achieved RMSEs of 0.668 L, 0.224 L, and 0.428 L/s for FVC, FEV₁, and PEF, respectively, using low-effort breathing maneuvers, and 0.499 L, 0.304 L, and 0.891 L/s using forceful exhalation maneuvers. These results demonstrate the potential of the DSP as a wearable, low-effort alternative for estimating lung function outside of conventional spirometry settings. Full article

(This article belongs to the Section Wearable Biosensors)

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15 pages, 6196 KB

Open AccessArticle

Fabrication of Co-Doped Covalent Organic Framework Nanosheets with Mild Interlayer Stress for Quantitative Detection of Alzheimer’s Disease Biomarkers

by Yubing Lv, Yanli Zhou, Zi Liu, Hui Dong, Hejie Zheng, Sihan Cheng, Xu Wang, Chaoran Lv and Maotian Xu

Biosensors 2026, 16(5), 271; https://doi.org/10.3390/bios16050271 - 8 May 2026

Abstract

Alzheimer’s disease (AD) seriously affects human health worldwide. Nicotinamide adenine dinucleotide (NADH) and glutamate are important biomarkers of AD, which play an indispensable role in the pathogenesis of AD. Herein, two ligands were used to synthesize a layered covalent organic framework (TPCOF) via [...] Read more.

Alzheimer’s disease (AD) seriously affects human health worldwide. Nicotinamide adenine dinucleotide (NADH) and glutamate are important biomarkers of AD, which play an indispensable role in the pathogenesis of AD. Herein, two ligands were used to synthesize a layered covalent organic framework (TPCOF) via amide bond formation, which was loaded with cobalt to obtain Co-TPCOF. TPCOF has a twisted 2D layered structure, along with large interlayer spacing and porosity, enabling precise Co coordination and stable loading of metal nanoparticles/enzymes to support electrocatalysis. A Co-TPCOF was immobilized on a screen-printed electrode (SPE) to catalyze the oxidation of NADH. After that, the oxidation product NAD⁺ of NADH and the NAD⁺-dependent dehydrogenase immobilized on the electrode jointly catalyzed the glutamate in the solution. COFs’ unique structures endow Co-TPCOFs with excellent NADH catalytic activity. The Co-TPCOF/SPE showed good linearity for NADH (10 nM-5 mM, LOD 7.07 nM) and GDH/Co-TPCOF/SPE for glutamate (50 μM-5 mM, LOD 3.74 μM). The biosensor can sensitively detect trace NADH and glutamate in human serum, providing an adequate technical means and theoretical reference for the pathological research of AD. Full article

(This article belongs to the Special Issue Advanced Electrochemical Biosensing: Materials, Mechanisms, and Applications)

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17 pages, 3782 KB

Open AccessArticle

A General Analytic Approach for Rapid Diagnostics by a Simple Algorithm for Fluorescence Single Molecule Counting

by Juiena Hasan and Sangho Bok

Biosensors 2026, 16(5), 270; https://doi.org/10.3390/bios16050270 - 8 May 2026

Abstract

Accurate biomolecule quantification at ultralow concentrations remains a major challenge because conventional ensemble assays report population averaged signals and therefore lose sensitivity in low-abundance regimes. Single molecule fluorescence counting can overcome this limitation by converting emission into discrete digital events, but practical implementation [...] Read more.

Accurate biomolecule quantification at ultralow concentrations remains a major challenge because conventional ensemble assays report population averaged signals and therefore lose sensitivity in low-abundance regimes. Single molecule fluorescence counting can overcome this limitation by converting emission into discrete digital events, but practical implementation is often hindered by manual inspection, limited reproducibility, and the complexity of machine learning based analysis. Here, we present a simple and general analytical framework for rapid single molecule detection based on a deterministic threshold algorithm that exploits the temporal signature of fluorescence blinking. The method operates directly on time resolved fluorescence image stacks, applies median filter-based noise suppression, and identifies candidate single molecule events from consecutive frame-to-frame intensity transitions without the need for training data or model fitting. Applied to Alexa Fluor 488, Alexa Fluor 647, and Rhodamine Red–X datasets, the approach reproduced the concentration dependent trends observed by manual counting, while providing more standardized detection under weak signal and high background conditions. Dye specific operating thresholds yielded robust counting behavior and preserved approximately linear concentration dependent response across the tested range. Compared with manual analysis, which required inspection of only selected grid regions, the automated workflow processed full movie stacks and reduced analysis time from ~3 h to ~20 min per concentration, corresponding to an approximately 9-fold gain in efficiency. These results establish an interpretable, computationally lightweight, and experimentally adaptable strategy for fluorescence single molecule counting that can support rapid diagnostics and provide a practical foundation for future extensions in automated localization, clustering, and real time molecular analysis. Full article

(This article belongs to the Special Issue AI/ML-Enabled Biosensing: Shaping the Future of Disease Detection)

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25 pages, 4009 KB

Open AccessReview

Evolution of Next-Generation Multiplex Lateral Flow Immunoassays: From Engineered Nanomaterials to AI-Driven Detection

by Tan-Thanh Huynh, Duc-Thang Vo and Trong-Nghia Le

Biosensors 2026, 16(5), 269; https://doi.org/10.3390/bios16050269 - 7 May 2026

Abstract

Decentralized diagnostics is undergoing a transformative shift from qualitative screening to high-precision quantification, driven by the clinical demand for rapid, point-of-care (POC) syndromic triage. Multiplexed lateral flow immunoassays (mLFIAs) serve as the foundational platform for this transition. However, their performance is limited by [...] Read more.

Decentralized diagnostics is undergoing a transformative shift from qualitative screening to high-precision quantification, driven by the clinical demand for rapid, point-of-care (POC) syndromic triage. Multiplexed lateral flow immunoassays (mLFIAs) serve as the foundational platform for this transition. However, their performance is limited by systemic factors such as fluidic lag, conjugate depletion, and spectral crosstalk. This review evaluates recent advances in engineered nanomaterials and artificial intelligence (AI)-driven detection as the dual pillars of next-generation multiplexing. The review covers different types of nanomaterial reporters—such as multicolor quantum dots, surface-enhanced Raman scattering nanotags, upconversion nanoparticles, surface-modified magnetic nanoparticles, and fluorescent nanodiamonds—that help address analytical challenges in lateral flow assays. We then discuss AI and machine learning methods, including convolutional neural networks, support vector machines, random forests, and transfer learning, that convert raw multi-channel signals into useful clinical data. Finally, we highlight the main challenges that still need to be addressed before these platforms can become WHO-ASSURED-compliant POC devices. The combination of engineered nanomaterial reporters and computational intelligence is transforming lateral flow assays into quantitative tools that can provide lab-quality clinical information at the POC. Full article

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

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17 pages, 2831 KB

Open AccessArticle

A Microcontroller-Based Device for Liquid Intake and Lick Analysis in Rodents

by Sikandar Ali, Massimo Ubaldi, Sofia Christina Gkolfinopoulou, Andrea Della Valle, Fabio Casarola, Adolfo Russo, Matteo Piersantelli and Roberto Ciccocioppo

Biosensors 2026, 16(5), 268; https://doi.org/10.3390/bios16050268 - 6 May 2026

Abstract

Drinking behavior is an important parameter in laboratory animal experiments. Traditionally, the amount of fluid consumed is recorded manually by the experimenter, who weighs the bottle at predetermined timepoints to calculate the intake. This approach is labor-intensive and involves human intervention, which can [...] Read more.

Drinking behavior is an important parameter in laboratory animal experiments. Traditionally, the amount of fluid consumed is recorded manually by the experimenter, who weighs the bottle at predetermined timepoints to calculate the intake. This approach is labor-intensive and involves human intervention, which can disturb the animals and affect their natural drinking patterns. Automated drinkometer systems have been developed to enable continuous and automated recording of fluid consumption. One advantage of these devices is that they allow researchers not only to measure total fluid intake but also to analyze the microstructure of drinking behavior (e.g., number of licks, bouts, and inter-bout intervals). However, these systems typically require dedicated cages equipped with proprietary drinkometers, making them highly expensive and often unsuitable for monitoring fluid consumption under standard home cage conditions. Here, we describe the development of a new cost-effective microcontroller-based device for continuous monitoring of liquid consumption and analysis of licking microstructure, which can be integrated with existing cages. The system is based on capacitive sensing technology for the assessment of liquid consumption. A customized bottle handler and capacitive sensor have been designed, which can be mounted on a standard 250 mL sized bottle for data acquisition. The device has been tested both in mice and in rats. The results demonstrated that for both animal species the system reproducibly and reliably recorded fluid consumption, also allowing for licking analysis. The data are acquired autonomously through custom devices and directly transferred to a computer for storage and analysis. Full article

(This article belongs to the Section Nano- and Micro-Technologies in Biosensors)

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26 pages, 24887 KB

Open AccessArticle

Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications

by Jiani Liu, Wang Gao, Chengxi Guo, Wenzhuo Cai, Ziyan Tang, Song Li, Yan Deng, Xiaoguang Qu and Zhu Chen

Biosensors 2026, 16(5), 267; https://doi.org/10.3390/bios16050267 - 5 May 2026

Abstract

Rapid and accurate pathogen detection serves as a core component in infectious disease prevention and control, clinical diagnosis and treatment, and public health surveillance systems. Although traditional detection methods have been widely adopted in clinical practice, they still exhibit significant limitations in terms [...] Read more.

Rapid and accurate pathogen detection serves as a core component in infectious disease prevention and control, clinical diagnosis and treatment, and public health surveillance systems. Although traditional detection methods have been widely adopted in clinical practice, they still exhibit significant limitations in terms of detection speed, throughput, automation levels, and adaptability to complex samples. In recent years, artificial intelligence (AI) technology has provided novel technical pathways for pathogen detection by leveraging its strengths in feature learning, pattern recognition, and multidimensional data modeling. The core contribution of this review lies in providing a novel, integrated analytical framework that overcomes the limitations of existing reviews, which often focus on a single modality (such as imaging alone or molecular diagnostics alone). Based on this framework, this paper systematically reviews AI research progress in pathogen detection, focusing on typical applications of machine learning and deep learning algorithms in analyzing imaging data, molecular diagnostic data, sensor signals, microscopic images, and multimodal data. It summarizes AI’s enabling value in enhancing detection sensitivity, specificity, automation, and point-of-care capabilities. Concurrently, this paper delves into key challenges facing AI-assisted pathogen detection, including data standardization, model generalization, interpretability, and clinical translation. It also outlines future trends toward intelligent, integrated, and clinically deployable applications. This paper aims to provide researchers and clinicians in the interdisciplinary field of artificial intelligence, biosensing, and clinical medicine with a comprehensive reference and roadmap for future development. Full article

(This article belongs to the Special Issue Materials and Techniques for Bioanalysis and Biosensing—2nd Edition)

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