Biosensors doi: 10.3390/bios16090472

Authors: Shuo Zhang Kangkang Xu Nan Zeng Jiansong Sun Qianrui Yang Qianke Zeng Zheng Ding Yanyu Lu Jian Zhao Mohamad Sawan Shan Fu Guoxing Wang Cheng Chen

Wearable near-infrared spectroscopy (NIRS) enables non-invasive hemodynamic monitoring, yet conventional CW-NIRS systems suffer from limited temporal resolution, scalp-only measurement, and mandatory manual tuning to compensate for optical heterogeneity across subjects and sites. This work develops NIRSLINK, a modular cascaded wearable NIRS system for high-speed multi-site hemodynamic acquisition. Its flexible probes adopt spring-floating optics with a standardized 30 mm optode separation, integrating dual 735/850 nm LEDs, silicon photodiodes, and a two-stage closed-loop tuning algorithm. A single probe achieves a peak sampling rate of 3 kHz, and up to eight cascaded probes form 52 valid channels, with a signal-to-noise ratio (SNR) of 78.61 ± 7.03 dB and an optical dynamic range (DR) of 101.32 ± 12.41 dB. Phantom experiments verify its millisecond temporal resolution and high sensitivity to blood flow and hemoglobin variations. In vivo trials, including the Valsalva maneuver, forearm occlusion, and two-back cognitive tasks, demonstrate simultaneous recording of hemoglobin concentration shifts, pulse waveforms, and beat-to-beat pulse transit times (PTTs). NIRSLINK supports hemodynamic measurements across multiple anatomical locations, including the forehead, forearm, upper arm, and thigh, covering both cranial and peripheral body regions. The embedded auto-tuning module stabilizes signals from the forehead, forearm, and other regions within the optimal ADC range without manual adjustment, preventing signal saturation and SNR degradation. This scalable adaptive platform overcomes critical drawbacks of traditional wearable NIRS, applicable to cognitive neuroscience, non-invasive cardiovascular assessment, and ambulatory physiological monitoring, and provides design references for multi-site optical sensors.

Biosensors doi: 10.3390/bios16090471

Authors: Pedro Jesús Rodríguez de Rivera Miriam Rodríguez de Rivera Fabiola Socorro Eduardo Garcia-Gonzalez Elisabetta De Nigris Jose A. L. Calbet Manuel Rodríguez de Rivera

In this study, we examined the thermal response of the vastus lateralis muscle using a skin calorimeter designed for localized measurements. Five healthy young male participants (20–23 years old) performed an incremental test on a cycle ergometer at 20 W·min−1 until exhaustion. The calorimeter’s thermostat was maintained at 30 °C. Ambient temperature was 23 ± 1 °C, and relative humidity ranged from 55% to 60%. The measured heat flow was modeled using functions relating mechanical power output to the thermal response. The model included the exercise phase, recovery, and sweat evaporation. The proposed model accurately reproduced the experimental data and supported a physiological interpretation of the main thermal effects. Two major contributions were identified and physiologically interpreted as muscle warming due to increased metabolic activity and a cooling effect likely linked to changes in blood perfusion. For an area of 2 × 2 cm2 and an incremental exercise of 20 W·min−1, the following values were obtained: (1) a resting heat loss of 150 ± 20 mW; (2) an exponential increase due to exercise of 0.4 ± 0.1 mW·W−1, with a time constant of 1.0 ± 0.3 min; and (3) a negative blood-flow contribution described by a function that, in the steady state, has a amplitude of −20 ± 9 mW. Since only five participants were included, correlations with anthropometric variables were treated as exploratory consistency checks. These results demonstrate the usefulness of the calorimeter for decomposing and quantifying muscle thermal dynamics during exercise.

Biosensors doi: 10.3390/bios16090470

Authors: Muhammad Saqib Elena I. Korotkova Kunquan Li Neda Firoz Mrinal Vashisth Amrit L. Hui Olga I. Lipskikh Pradip Kumar Kar

The integration of artificial intelligence (AI) and machine learning (ML) with electrochemical sensing has revolutionized analytical diagnostics by overcoming traditional limitations such as signal drift, peak overlapping, and matrix interference. However, despite the exponential growth of this field, a unified framework evaluating translational feasibility remains absent. This review critically analyzes AI/ML architectures applied to electrochemical sensors and biosensors from 2016 to 2025. To the best of our knowledge, this work introduces the first coded Network Evidence Map to quantitatively map the co-occurrence of methodological strengths, weaknesses, and translational barriers across the examined literature. The analysis reveals that while deep learning and ensemble models excel in signal deconvolution and multiplexing, the field is severely constrained by systemic bottlenecks. Network pathways demonstrate that over 83% of studies lack uncertainty quantification, and data scarcity coupled with restricted data-sharing policies critically undermines model reproducibility. Furthermore, batch-to-batch hardware variability measurably co-occurs with the opacity of black-box algorithms, hindering regulatory approval. We conclude that advancing from laboratory proof-of-concept to real-world point-of-care deployment necessitates a paradigm shift toward open-source electrochemical repositories, explainable AI (XAI), physics-informed machine learning, and hardware-software co-design.

Biosensors doi: 10.3390/bios16090469

Authors: Matheus Willian Sprotte Pedro Bertemes Filho

Wearable glucose monitoring demands low-power local processing, but conventional neural networks rely on energy-intensive multiply–accumulate (MAC) operations that limit battery life. This study shows that a Spiking Neural Network (SNN), built on a regression-adapted Leaky Integrate-and-Fire (LIF) neuron, can estimate blood glucose from multi-frequency bioimpedance and auxiliary biosignals with clinically auditable accuracy at low computational and memory cost. Using data from 98 patients (717 measurements, eGluco3 device, Azambuja Hospital, Brusque, Brazil) evaluated by 5-fold walk-forward cross-validation under ISO 15197:2013, three main findings emerge. First, a new calibration method—the Patient Fingerprint, built from each patient’s first K sensor readings—outperforms conventional one-hot patient encoding (14.2 ± 2.6 mg/dL vs. 15.4 ± 3.3 mg/dL mean absolute error) and, unlike one-hot, requires only these K readings rather than the patient’s presence in the training set; a leave-patients-out analysis confirms that the fingerprint captures individual physiology and that unseen-patient accuracy improves with calibration depth but remains clinically insufficient (MAE 94.8→65.9 mg/dL from K=3 to K=5), positioning clinical-grade cross-patient generalization on a larger cohort as the primary scaling axis. Second, the direct-injection fingerprint model reaches 100% of the samples within Consensus Error Grid Zones A+B across all validation folds (the rate-coding variant reaches 98.8%, just below the 99% Criterion B threshold), without requiring any demographic or clinical metadata; sensor history alone renders such records redundant; and Criterion A, however, stays below the 95% normative threshold, so the results support clinical safety rather than formal certification. Third, replacing the analog input encoding with a multiplication-free rate-coding scheme removes all first-layer MAC operations at a cost of 2.7 mg/dL additional error; because the additional microticks raise the total operation count, this defines a design lever whose energy payoff is specific to neuromorphic hardware rather than a net saving on conventional microcontrollers. Together, these results demonstrate that SNNs offer a clinically auditable, self-calibrating, and memory-efficient path to continuous glucose estimation on embedded wearable devices.

Biosensors doi: 10.3390/bios16090468

Authors: Chinnawut Pipatpanukul Komkrisd Wongtimnoi Laurent Mezeix Santi Phosri

Safe transfusion depends on rapid, accurate ABO typing, yet reference methods require centrifuges and instrumentation unavailable at the point of need. Here, a paper-based ABO-RhD typing device built on a covalent, optically silent surface chemistry is reported. Aminosilane (APTES) and epoxysilane (GPTMS) functionalisation of Whatman cellulose was compared by water contact angle, energy-dispersive X-ray spectroscopy (SEM-EDS) and infrared spectroscopy (FT-IR) across two paper grades, three silane concentrations (5, 10 and 20% v/v) and three reaction times (1–6 h). APTES produced a strongly hydrophobic layer that impeded aqueous wicking, and its glutaraldehyde activation generated a red-brick chromophore incompatible with a red-channel readout. GPTMS coupled antibodies in a single mild step, without a crosslinker or visible chromophore, while preserving wicking. GPTMS (10% v/v, 3 h, Whatman No. 4) with a six-cycle 100 µL saline wash was selected; antibodies were immobilised in a four-zone layout (anti-A, anti-B, anti-D and control) within a 3D-printed two-compartment housing that traps agglutinated cells while free cells wash through. On 80 EDTA clinical blood samples (20 each of groups A, B, AB and O) the device classified every sample correctly (accuracy 100%; 95% confidence interval 95.4–100%), with visual and instrumented reads in full agreement. All samples were RhD-positive, so the anti-D channel is validated here for the positive call.

Biosensors doi: 10.3390/bios16090467

Authors: Boli Pan Shuo Ding Yingbo Geng Jiacheng Liang Fali Li Gang Wang Xirong Li Yanbin Dong Rihui Li

Objective: Recent advances in dry electrode EEG have enabled rapid setup and recording in unconventional scenarios. However, past developments were primarily driven by brain–computer interfaces (BCI), leaving their comparability to wet electrodes in clinical and daily life applications an open question. Here, we developed a new dry EEG system and systematically benchmarked its performance against a commercial wet EEG system across various tasks. Methods: Participants (n = 19) underwent simultaneous recording using both devices. We first collected resting-state EEG under both eyes-closed and eyes-open conditions, followed by a steady-state visual evoked potential (SSVEP) task at different flicker frequencies and a motor imagery (MI) task. System performance was evaluated using power spectral density (PSD), signal to noise ratio (SNR), event-related spectral perturbation (ERSP), and single-trial classification accuracy. Results: The two systems performed similarly across different tasks. During the resting state, no statistically significant differences were observed between the two systems in the PSD of the five frequency bands (p > 0.05 in all cases). Similarly, SNR in the SSVEP task showed no significant differences at 8 Hz, 10 Hz, and 12 Hz after correction. For cognitive tasks, classification accuracies were comparable (SSVEP: dry 80.08% ± 7.1% vs. wet 81.10% ± 6.5%; MI: dry 72.46% ± 3.89% vs. wet 70.7% ± 2.37%). Conclusions: The developed dry EEG system can effectively record electrophysiological measurements commonly employed in research and clinical settings, with quality comparable to that of traditional wet EEG systems.

Biosensors doi: 10.3390/bios16090465

Authors: Niusha Hassandoost Leslie Munoz Kerrigan Kotecki Irina V. Nesterova

Accessible molecular diagnostics is fundamental to effective healthcare. While most current point-of-care devices detect only the presence of a molecular biomarker(s), biomarker quantification can be equally important for decision-making on disease treatment and containment. Here, we present a diagnostic platform that enables the equipment-free quantification of molecular biomarkers with the simplicity of a binary (yes/no) readout. This capability is achieved by integrating a stoichiometric quantitative approach with widely available and easy-to-use lateral flow dipsticks. To implement the approach, we engineer negative cooperativity into target–probe binding interactions for oligonucleotide targets as a model system. The resulting threshold-based semi-quantitative assay with lateral flow dipsticks quantifies targets in the low-nanomolar range and operates reliably in complex biological backgrounds. A key advantage of this platform is its potential adaptability to new and emerging targets: repurposing will require only reagent redesign, without the need for additional fabrication.

Biosensors doi: 10.3390/bios16090466

Authors: Matthew A. Crocker Kevin A. White Mahdieh Darroudi Vishnu Saket S. Bapanapalli Charles S. Lipscomb Benjamin S. John Brian N. Kim

The simultaneous measurement of biopotential and neurochemical signals provides a comprehensive view of the brain. Yet, most neural interfaces record solely biopotential or neurochemical activity. This work presents a complementary metal-oxide-semiconductor (CMOS) analog front-end (AFE) chip that integrates 32 biopotential amplifiers and 32 neurochemical amplifiers for parallel recording from 64 electrodes. The biopotential amplifier is a two-stage design providing a gain of 57.1 dB, a bandwidth of 0.4 Hz–6.2 kHz, and 6.7 µVRMS input-referred noise (20 kHz sampling rate). The neurochemical amplifier is a rail-to-rail folded-cascode operational amplifier with selectable transimpedance gain (91.9 kΩ to 851.9 kΩ), a dynamic range of ±15 μA to ±2 μA, respectively, a bandwidth of 12.6 kHz, and input-referred noise as low as 46.3 pARMS (20 kHz sampling rate). The neurochemical amplifiers are designed for fast-scan cyclic voltammetry (FSCV) measurements. I/O complexity is minimized using a time-division multiplexing scheme for readout, enabling straightforward scalability. The chip is fabricated using a 0.35-µm CMOS process and occupies a 3.0 × 8.3 mm2 area. In vitro recordings of catecholamines and neural spikes validate the chip’s function. The chip enables scalable, low-noise, bimodal neural recording, supporting investigations into the dynamics between neuronal biopotential activity and neurochemical signaling.

Biosensors doi: 10.3390/bios16090464

Authors: Shuyuan Zhao Wen Lei Juan Li Jingjing Xia

As a noninvasive liquid biopsy approach, extracellular vesicle (EV)-based detection offers significant advantages in reflecting real-time tumor dynamis and overcoming the limitations of conventional tissue biopsy. EVs, nanoscale vesicles secreted by cells, carry diverse biomolecules such as proteins and nucleic acids, playing key roles in tumor progression, metastasis, and immune evasion, and have emerged as promising biomarkers for cancer liquid biopsy. Surface-enhanced Raman spectroscopy (SERS), characterized by high sensitivity, resistance to photobleaching, minimal sample consumption, and multiplexing capability, has shown great potential in EV analysis. This review systematically summarizes current methods for EV isolation, characterization, and storage, with a focus on label-free and label-based SERS detection strategies for early cancer diagnosis, treatment response monitoring, and prognosis evaluation. Furthermore, the integration of SERS with machine learning and deep learning algorithms has substantially improved diagnostic accuracy and cancer subtyping. Despite remaining challenges, such as optimization of SERS substrate performance, intelligent processing of Raman spectral fingerprints, and clinical translation, EV-based SERS technology holds great promise for precision oncology.

Biosensors doi: 10.3390/bios16090463

Authors: Amit Kumar Shakya Mantas Grigalavičius

In this research, a high-performance plasmonic refractive index (RI) biosensor based on an external metal deposition (EMD) technique and photonic crystal fiber (PCF) platform for potential cancer detection is presented, investigated, and linked with real-time cancer cells. Variations in the RI of biological fluids are closely associated with pathological conditions, including cancer, due to changes in cellular composition and biomolecular concentration. The proposed tetra-core PCF–SPR biosensor operates within the biologically relevant RI range of 1.33–1.37, enabling the detection of subtle RI variations corresponding to various cancerous cells. The sensing mechanism of the proposed sensor is based on surface plasmon resonance (SPR) and analyzed using coupled mode light theory for both x- and y- polarized modes. Key sensing performance parameters, including confinement loss (CL), wavelength sensitivity (WS), amplitude sensitivity (AS), sensor resolution (SR), and figure of merit (FOM) are systematically evaluated. The biosensor reports a WS of 9769 and 9069 nm/RIU for x-pol. and y-pol., respectively, AS of 623.182 and 645.087 RIU−1 for x-pol. and y-pol. respectively, SR in the order of 10−5 RIU, coefficient of determination (R2) of 0.97 and 0.96, and FOM of 60.17 and 53.01 RIU−1 for x-pol. and y-pol., respectively. Thus, the proposed PCF–SPR biosensor exhibits a dynamic range of 0.04 RIU. The sensing results demonstrate high sensitivity and strong resonance characteristics, indicating the capability of the proposed biosensor for label-free and non-invasive detection of cancer-associated RI changes in biological fluids. Thus, the presented biosensor offers a promising approach for the highly sensitive label-free detection of early-stage cancer cells by photonics sensing application.

Biosensors doi: 10.3390/bios16090462

Authors: Lyubov S. Kuznetsova Kristina D. Ivanova Jun Zhang Vyacheslav A. Arlyapov

Redox-active polymers have become an important component of second-generation electrochemical biosensors, solving the problem of efficient charge transfer between the biological recognition material and the electrode surface. In this review, we discuss the basic design principles, electron transfer mechanisms, and synthesis strategies from the perspective of biosensor applications. Three main classes of redox centers are considered—metal complexes, metallocenes, and organic radicals—as well as polymer matrices, and the factors affecting their stability and operability are discussed. Particular attention is paid to hybrid nanocomposites based on carbon nanotubes, graphene, and metal nanoparticles. The review concludes that despite significant advances in molecular design and the development of nanocomposites, the commercialization of biosensors based on redox polymers is hindered by unresolved issues related to biofouling, metal center instability, and low reproducibility. This emphasizes the need for standardized synthesis and integration of machine learning-based design to achieve a balance between electron transfer kinetics, biocompatibility, and operational properties.

Biosensors doi: 10.3390/bios16090461

Authors: Minhui Liang Yilong Zhou Dawei Ming Jiawei Lyu Jianwei Zhong Han Li Lin Lin

High-throughput microfluidic deformation assays enable label-free single-cell mechanophenotyping by quantifying how cells deform under controlled hydrodynamic loading. These approaches commonly extract deformation-related observables, such as projected area, axis ratio, and deformation index, and use them as indicators for cellular mechanical properties. However, deformation is not solely determined by stiffness; it is a coupled outcome of cell size, local hydrodynamic stress, and intrinsic mechanical response. Therefore, we present a simulation-based microfluidic framework for estimating region-dependent apparent Young’s modulus (E, a quantitative indicator characterizing cellular mechanical stiffness) from diameter–deformation measurements at the single-cell level. A three-region microfluidic channel is designed to impose distinct hydrodynamic loading conditions, while numerical simulations establish quantitative maps linking cell diameter, deformation, and E. Based on these results, region-specific nonlinear surface models are constructed to invert experimental diameter–deformation measurements into E values. Finally, application to primary T cells and K562 cells demonstrates clear region-dependent differences in E, highlighting the influence of local loading conditions on inferred mechanical properties. Overall, this work provides a simplified but practical route for transforming deformation-based phenotypes into quantitative, loading-aware mechanical parameters for single-cell analysis.

Biosensors doi: 10.3390/bios16090460

Authors: Dalia Elsheakh Ratshih Sayed Hebatullah H. Draz Ghada H. Ibrahim Heba Shawkey

Diabetes is a major worldwide health concern, which emphasizes the critical need for precise and continuous glucose monitoring devices. This paper introduces a novel, non-invasive method for continuous blood glucose monitoring using on-chip multi-arm sensors designed as earbuds by using UMC 0.18 μm technology. The proposed sensor uses the dielectric characteristics of the earbud to detect variations in glucose levels while operating at various resonant frequencies, including 32, 42, 64, and 94 GHz. The sensitivity of the proposed method was evaluated using a reflection coefficient criterion of ∣S11∣≤−6 dB, confirming its ability to achieve accurate detection when implemented within an earbud device. A 3D electromagnetic high-frequency structure simulator (HFSS) is used to validate the simulation. Only |S11| data are used to determine the glucose concentrations in the blinded prediction group. The results demonstrate a strong correlation between sensor responses and glucose levels. The sensor achieved a sensitivity of 12.4 MHz/mg/dL, 6 dB/mg/dL. Moreover, the earbud’s homogeneous tissue architecture and naturally low eccrine sweat gland density lessen susceptibility to confounding physiological variables commonly observed in microwave-based glucose detection. As a major advancement in biomedical sensing technology, this wearable system provides a precise and useful method for non-invasive glucose monitoring.

Biosensors doi: 10.3390/bios16090459

Authors: Fatih Pekdemir İzzet Koçak

Sulfathiazole (STH) pollution has been increasing, with a strong need for a sensitive and selective analysis method. In this paper, a three-component fluorescent sensing platform with Mn2+-doped ZnS quantum dots in a metal–organic framework coated with a surface molecularly imprinted polymer (Mn-ZnS-MOF-MIP) for selective sensing of sulfathiazole is proposed. This synergistic sensor platform combines Mn-ZnS-insensitive emission, MOF-assisted analyte enrichment, and imprinting-based molecular recognition. The sensing platform displays a characteristic turn-off fluorescence with a dominant static quenching mechanism. Under optimal conditions, a broad sensing range with a low detection limit of 13.75 nM can be obtained. Outstanding selectivity among similar sulfonamides, biomolecules, and metal ions were achieved with high reproducibility, stability, and reusability. This sensing platform was shown to analyze sulfathiazole accurately in aqueous samples and blood serum with high recoveries, within the range of 90.00–104.24%, compared to high-performance liquid chromatography methods.

Biosensors doi: 10.3390/bios16090458

Authors: Liu Wang Yang Zhou Wenjia Li Jian Zhao

Multimodal medical image fusion combines anatomical and functional information from different imaging modalities. However, existing methods often struggle to preserve fine anatomical structures while incorporating complementary functional information. To address this problem, we propose TAPGFusion, an anatomy-aware multimodal medical image fusion network. It employs a Multi-Scale Encoder to capture fine local details and broad anatomical structures through parallel convolutions with different receptive fields. A Detail-Enhanced Attention Block further refines the extracted features through channel, spatial, and pixel attention. In addition, a Physiological Prior-Guided Attention Block dynamically balances anatomical and functional features using MRI-conditioned prior information and edge constraints. The main contribution of TAPGFusion is a unified framework that jointly addresses multi-scale feature representation, fine-grained feature selection, and spatially adaptive anatomical–functional fusion. Extensive experiments on three public medical imaging datasets demonstrate the effectiveness and robustness of the proposed method. TAPGFusion achieves CC values above 0.83 and SSIM values above 0.78 on the CT–MRI, PET–MRI, and SPECT–MRI fusion tasks. These results indicate that the proposed method effectively preserves anatomical structures while integrating complementary functional information.

Biosensors doi: 10.3390/bios16090457

Authors: Kudratjon Zohirov Sardor Boykobilov Gulmira Pardayeva Nilufar Akhmedova Dilobar Ilmurodova Iroda Uralova Zavqiddin Temirov Rashid Nasimov

Electromyography (EMG)-based sensing is an important tool for assessing neuromuscular activity and monitoring athlete development; its reliability depends on electrode placement, signal quality, and accurate identification of muscle activation periods. This study proposes an intelligent EMG sensing framework integrating preliminary electrode placement assessment, muscle activity detection, feature extraction, and regression-based progression prediction. A placement assessment indicated that positioning the electrode adjacent to the innervation zone produced the highest RMS under the tested conditions. A two-stage activity detection method based on clustering and probabilistic modeling achieved an average error of 1.5% and a temporal deviation of 19 ms. Nine time-domain EMG features extracted from the detected activity segments were used to characterize athlete progression and estimate the time required to reach a reference neuromuscular profile. Among the methods, Linear Regression provided the best fit to the data, obtaining R2 = 0.987 and RMSE = 4.21 and suggesting a predominantly linear relationship between the EMG-derived features and training duration within the dataset. However, these results were obtained from only six longitudinal observation periods for a single representative athlete, with each period represented by a 90-dimensional EMG feature vector derived from the ten movement classes. Therefore, the results should be interpreted as preliminary, athlete-specific goodness-of-fit findings rather than evidence of generalizable predictive performance. Validation using larger longitudinal cohorts and independent datasets is required. The proposed framework is compatible with future IoT-enabled wearable and edge-computing architectures; however, hardware-level implementation was beyond the scope of this study.

Biosensors doi: 10.3390/bios16090456

Authors: Josep J. Centelles Santiago Imperial

Malaria remains one of the most significant infectious diseases worldwide, requiring rapid, sensitive, and accessible diagnostic tools to improve disease management and control. Conventional diagnostic methods, including microscopy, rapid diagnostic tests, and nucleic acid amplification techniques, present limitations in sensitivity, specificity, cost, or field applicability. This review examines the emerging role of aptamer-based biosensors (aptasensors) as innovative alternatives for malaria detection. Aptamers are synthetic nucleic acid ligands that offer high affinity and specificity toward malaria biomarkers while providing advantages over antibodies, including improved stability, lower production costs, and ease of chemical modification. The review discusses aptamer selection methodologies, major Plasmodium biomarkers targeted for detection, and the integration of aptamers into electrochemical, optical, magnetic, and microfluidic biosensing platforms. Current advances demonstrate the potential of aptasensors to enable highly sensitive, selective, and portable point-of-care diagnostics for malaria surveillance and management.

Biosensors doi: 10.3390/bios16090455

Authors: Hyungjung Kim Woohyun Jin Do-Kyung Kim Jaewoo Kim Jeongwoo Park

There is an increasing demand for ultrasound imaging technologies, particularly wearable and portable systems, for continuous physiological monitoring applications. Although some recent flexible ultrasound devices have adopted polymer encapsulations, typical rigid transducer designs still include metal housings and costly electrodes, contributing to increased device weight and fabrication cost. To address these limitations, we developed an aluminum-electrode/3D-printed polymer-housing ultrasound transducer (APUT) utilizing a polyvinylidene fluoride piezoelectric film. Compared to a gold-electrode/metal-housing ultrasound transducer, the APUT material costs and total weight were approximately 66% and 86% lower, respectively. Acoustic evaluation revealed a center frequency of 24.5 MHz and a fractional bandwidth of 60.9%, with axial and lateral resolutions of 51 and 152 μm, respectively. Furthermore, during a 3-h pulsed operation test, the APUT exhibited an initial increase in capacitance followed by a relatively stable response, with no progressive surface-temperature increase detected within the accuracy of the measurement method. Finally, successful ex vivo imaging of chicken breast tissue confirms the APUT’s biomedical applicability, highlighting its potential as a wearable, portable, and disposable ultrasound platform.

Biosensors doi: 10.3390/bios16080454

Authors: Burcu Kolukisa Birgec Beyza Toprak Alexander Balfour Mullen

Access to in-laboratory polysomnography (PSG) is restricted by prolonged waiting lists, and first-night effects can distort typical sleep architecture. This study evaluates the PneumoWave biosensor as a scalable, unobtrusive alternative for longitudinal single-channel home sleep apnea monitoring. Over three nights in an uncontrolled home environment, the biosensor’s respiratory rate agreement was evaluated against smartwatch-derived respiratory rate estimates, whilst apnea/hypopnea detection was compared with concurrent pulse oximetry. PneumoWave and smartwatch devices demonstrated good correlation (r = 0.870; p < 0.001). The PneumoWave device showed strong measurement agreement and provided a highly predictive screening pathway for patients with intermediate-to-high obstructive sleep apnea (OSA) risk. Longitudinal analysis confirmed consistent multi-night performance without first-night effect biases (ICC = 0.956, p < 0.001). Furthermore, its intuitive design yielded zero patient-induced setup errors, highlighting its operational robustness for self-administered use. Combining this continuous chest wall monitor with the STOP-Bang clinical questionnaire has the potential to provide an effective predictive screening pathway, improving community OSA screening and assisting clinical triage. Further validation against full polysomnography is warranted before clinical adoption.

Biosensors doi: 10.3390/bios16080453

Authors: Sergio Bravo-González Luisa María Reyes-Cortés Kristen Aideé Pérez-Alvarez Grissel Trujillo-de Santiago Mario Moisés Álvarez

Cancer, the second leading cause of death worldwide, is a significant global challenge, and widespread, accessible, and early diagnostics are recognized as the most cost-effective strategies for reducing cancer burdens. Point-of-care (POC) systems offer an attractive alternative by enabling rapid and cost-effective diagnoses. We introduce a novel POC strategy for cancer biomarker identification based on monitoring the isothermal amplification of relevant cancer markers using a portable Arduino-based loop-mediated isothermal amplification (LAMP) system. The trajectory of the LAMP reaction during the first 3 min of the reaction is used as an indicator of the rate of amplification (defined as the mP3 value). We obtained sets of mP3 values that showed statistically significant differences in the genetic expression of four genes (ESR 1, PGR, Her2, and Ki67) within and between tissue spheroids derived from the MCF7, MDA-MB-231, Du145, and BJ fibroblast cell lines. We then used principal component analysis and clustering techniques to demonstrate that the mP3 value sets derived from the expression of the four selected genes are sufficient to distinguish tissue spheroids derived from four different commercial cell lines. Further qPCR and immunostaining assays confirmed the quantitative LAMP (qLAMP) experimental trends. The immunostaining results were consistent with previous literature reports and with our qLAMP and qPCR results. We present a proof-of-concept demonstration of the use of a LAMP-based POC platform for the identification or discrimination of cancer tissues. Our strategy can be extended to other diseases associated with altered gene expression in body tissues or fluids.

Biosensors doi: 10.3390/bios16080451

Authors: Chen Liu Bolin Mai Kaiqi Wang Xiaomin Chen Yinling Sun Honghai Liu Honggen Du Yixuan Sheng Shao Chen

Adolescent idiopathic scoliosis (AIS) is associated with central neural control and peripheral muscle abnormalities. However, dynamic cortico-muscular coupling (CMC) during fatigue remains poorly understood. We used synchronous 32-channel electroencephalography and 32-channel high-density electromyography (HD-EMG) to record data from 20 adolescents with AIS and 20 healthy controls during the Biering–Sørensen test. Wavelet coherence and topographic mapping were used to characterize CMC and its spatial distribution. Descriptive analyses indicated group- and fatigue-related patterns in HD-EMG activation, cortical connectivity, and CMC topographies. For the prespecified C3/C4 regional analyses, effect sizes, 95% confidence intervals, exact p values, and false-discovery-rate-adjusted p values are reported to support transparent interpretation. These multimodal observations provide exploratory physiological patterns that require confirmation in larger studies with prespecified primary outcomes.

Biosensors doi: 10.3390/bios16080452

Authors: Yu Zhang Quan Shao Haihong Yang Xiaosong Ren Xiaolin Peng

Complex inter-subject variability induces severe distribution shifts in the physiological features of electroencephalography (EEG) for air traffic controllers (ATCOs). These inter-subject shifts limit the generalization and interpretability of passive brain–computer interfaces (pBCIs) during cognitive workload decoding. To address this, a Dynamic Microstate-Guided Graph Convolutional Network (DMG-GCN) is proposed for robust cross-subject workload recognition. This approach utilizes a Dynamic Selective Kernel Temporal Convolutional Block (DSK-TCB) to adaptively extract multi-scale temporal–spectral dynamics, while concurrently constructing a time-evolving adjacency matrix via a Microstate-Guided Dynamic Graph Block (MG-DGB) to disentangle topological sub-networks. A spatiotemporal graph convolution module then aggregates these representations, and a temporal self-attention mechanism focuses on task-critical transition moments. Extensive experiments on simulated multi-level air traffic control tasks demonstrate that the proposed model achieves an overall average accuracy of 80.30% and an average F1-score of 78.63% in cross-subject evaluations, significantly outperforming state-of-the-art baselines. Moreover, an exploratory interpretability analysis suggests that the extracted topological sub-networks exhibit spatial patterns consistent with specific brain network reorganizations, which encompass the transition from global distributed monitoring to temporal multimodal integration and parietal–occipital parallel processing during workload regulation. The framework provides a robust and analytically transparent pBCI solution for adaptive automation in modern aviation.

Biosensors doi: 10.3390/bios16080450

Authors: Marco Merino-Treviño Ana Beatriz Morales-Cepeda Hernán Peraza-Vázquez Edgar Onofre-Bustamante

Recently, plant-based bioelectronic systems have been explored for environmental sensing applications. However, their intrinsically low electrical conductivity often limits signal sensitivity and measurement reliability. In this work, the electrothermal behavior of living Epipremnum aureum plants incorporating Ag/AgCl nanoparticles supported on nanocellulose was investigated. Electrical and thermal responses were simultaneously measured under controlled environmental conditions using external shunt resistances of 1, 10, 100, and 1000 Ω. Compared with the control without nanoparticle incorporation, the nanoparticle-incorporated plant exhibited stronger electrical responses and distinct electrothermal behavior over the studied temperature range. The measured signals showed nonlinear responses, temporal asymmetry, and resistance-dependent modulation, suggesting changes in charge transport within the plant tissues. Silver-enriched regions and the co-detection of chlorine within the nanoparticle-incorporated plant tissues were identified by environmental scanning electron microscopy and energy-dispersive X-ray spectroscopy. Five machine-learning regression models were trained to estimate temperature using the measured electrothermal voltage signals as predictors. The best-performing model, MLP FitRNet, achieved a mean absolute error of 0.598 °C, a root mean square error of 0.748 °C, and an R2 value of 0.974. These results demonstrate the potential of nanoparticle-incorporated biohybrid plant systems for electrothermal signal analysis and data-driven temperature estimation, while providing a foundation for future intelligent environmental monitoring applications.

Biosensors doi: 10.3390/bios16080449

Authors: Se Eun Kim Hye Kyu Choi Jin-Ha Choi

Early cancer detection can expand treatment options and improve patient survival, but it requires tests that can be repeated with minimal patient burden. Urine and saliva can be collected non-invasively and may contain cancer-associated nucleic acids, proteins, and extracellular vesicles. Clinical analysis of these body fluids is complicated by low biomarker abundance, inter-individual variation, and matrix components that interfere with surface-based sensing. Plasmonic nanoarray biosensors address some of these analytical constraints by concentrating local electromagnetic fields, supporting multiplexed optical readout, and accommodating surface chemistry and microfluidic handling. This review examines nanoarray architectures, fabrication methods, surface functionalization, and signal generation for cancer-associated biomarkers in urine and saliva. Localized surface plasmon resonance, surface-enhanced Raman scattering, and metal-enhanced fluorescence are discussed together with applications to bladder, prostate, pancreatic, oral, and head-and-neck cancers. Remaining barriers include biofouling, pre-analytical variation, fabrication reproducibility, limited validation using authentic biofluids, and incomplete sample-to-answer integration. Addressing these challenges through standardized biofluid processing, scalable nanoarray fabrication, and integrated microfluidic platforms will be essential for translating plasmonic nanoarray biosensors into clinically applicable cancer screening tools.

Biosensors doi: 10.3390/bios16080448

Authors: Ahmet Akif Kızılkurtlu Ali Akpek

Continuous glucose monitoring has established that a molecular signal can be repeatedly measured in daily life and translated into clinically meaningful action. The next frontier is broader: wearable biosensors that monitor metabolites, electrolytes, hormones, drugs, nutrients, inflammatory markers, and tissue-state indicators beyond glucose. This review proposes an artificial intelligence (AI)-ready framework for multimodal wearable biochemical monitoring. We organize the field as a complete measurement chain that links biofluid access, sampling chronology, flexible biointerfaces, molecular recognition, signal conditioning, metadata capture, sensor fusion, clinical validation, and lifecycle governance. The central argument is that the clinically useful variable is rarely a raw current, potential, optical intensity, or spectrum; it is a quality-controlled, context-aware, and uncertainty-aware digital biomarker. We compare sweat, interstitial fluid, saliva, tears, wound exudate, and breath condensate; evaluate enzymatic, ion-selective, affinity, transistor, optical, and spectroscopic sensing strategies; synthesize representative high-impact studies; and define minimum metadata, validation metrics, and translation gates. The review highlights recurring gaps in biofluid validity, real-world robustness, reference-comparator alignment, subgroup evidence, and algorithmic governance. We conclude with practical design rules for converting flexible biochemical wearables from attractive prototypes into clinically credible intelligent biosensing systems.

Biosensors doi: 10.3390/bios16080447

Authors: Daniel Martín Luis Orta Akaitz Dorronsoro Diego Ruano Alberto Yúfera Paula Daza

The bystander effect describes the induction of responses in non-targeted cells through cell signaling by directly stimulated cells. While this phenomenon has been extensively studied in the context of ionizing radiation, its occurrence following electrical stimulation (ES) remains poorly understood. Conditioned medium from N2a neuroblastoma cells exposed to voltage-controlled biphasic pulses at 500 mV/mm and 100 Hz induced neuronal differentiation in non-stimulated cells through an ES bystander effect. Bystander medium promoted morphological changes associated with neuronal differentiation, including increased neurite outgrowth and a reduction in the proliferation marker KI-67, indicating that the effects of ES extend to neighboring non-targeted cells. Molecular analysis revealed increased expression and secretion of interleukin-6 (IL-6) following ES, while neutralization of the IL-6 receptor inhibited the effects of ES, highlighting the role of IL-6 as a key mediator of this effect. We provide the first evidence that ES promotes a differentiation-associated bystander effect mediated by IL-6.

Biosensors doi: 10.3390/bios16080446

Authors: Kirti Shila Sonkar Vito Levi D’Ancona Jade Cramp Hannah Shilling Ellie Giles Tyler Howell-Bray Becky Fillingham Merit E. Cudkowicz Avindra Nath Jeffrey D. Rothstein Robert Bowser Barbara Borroni James D. Berry Ghazaleh Sadri-Vakili Emanuele Buratti Ian P. Thrippleton

TDP-43 dysfunction is a defining feature of amyotrophic lateral sclerosis (ALS), yet no biofluid biomarker directly measures its functional activity. We developed a serum-based homogeneous time-resolved FRET (hTR-FRET) assay that quantifies TDP-43 RNA binding activity using synthetic UU-rich RNA probes. We analyzed 1080 serum samples from controls, sporadic ALS, and genetic subgroups (C9orf72, SOD1) across multiple biorepositories. Cross-sectionally, TDP-43 functional activity was elevated in ALS (mean 390 a.u.) versus controls (302 a.u.), yielding AUC = 0.79. Genotype means were 392 a.u. (sporadic), 382 a.u. (C9orf72), and 323 a.u. (SOD1); a 366 a.u. threshold achieved 95% specificity against controls. Longitudinally, Target ALS showed a modest but significant inverse correlation between TDP-43 activity and ALSFRS-R, while other cohorts exhibited similar non-significant trends. Elevated signal in serum likely reflects increased extracellular release of probe-competent TDP-43 species during cell death and exosomal shedding, rather than restored intracellular nuclear splicing function. This assay provides a proof-of-concept platform for the direct functional measurement of probe-competent TDP-43 species in serum. While it demonstrates moderate group-level discrimination, individual diagnostic performance requires prospective validation. The assay may support exploratory applications in genotype stratification and progression monitoring in future clinical studies.

Biosensors doi: 10.3390/bios16080445

Authors: Kuan-Hsun Huang Yu-You Liu Chia-Chien Wu Chia-Ling Chen Jie-Lun Hsieh Lung-Hsiang Chuo Hsiang-Chen Wang

Recent statistics reported by the World Health Organization and the International Agency for Research on Cancer indicate that the global incidence of bladder cancer has continued to increase in recent years, particularly in industrialized countries. Therefore, the timely diagnosis of early-stage bladder cancer is of great clinical importance for improving patient prognosis and treatment outcomes. In this context, computational optical sensing frameworks that integrate Spectrum-Aided Visual Enhancer (SAVE) technology with cystoscopy have attracted significant attention to overcome the limitations of conventional visual data interpretation. In this study, an AI-driven optical biosensing framework was evaluated using 1372 white-light cystoscopy (WLC) images of bladder cancer (RGB-WLC) collected in collaboration with Chung Shan Medical University Hospital. Hyperspectral conversion technology was applied to extract precise spectral information from the white-light images. Subsequently, dimensionality reduction was performed based on the characteristic wavelengths of narrow-band imaging cystoscopy at 415 nm and 540 nm to generate hyperspectral reconstructed narrow-band images. The images were categorized into Ta stage (Ta), above T1 stage (Above T1), and four additional classes. The dataset was divided into training and testing sets to establish both a standard white-light cystoscopy model (RGB-WLC) and an advanced hyperspectral biosensing model utilizing the YOLOv8 architecture for enhanced pattern recognition. Model performance was evaluated using sensitivity, F1-score, and overall accuracy. The standard RGB-WLC model achieved an accuracy of 0.852, whereas the SAVE-based biosensing model achieved an accuracy of 0.948, representing an improvement of approximately 11.27%. The results demonstrate that combining algorithmic hyperspectral reconstruction with deep learning architectures effectively addresses the challenges of clinical data interpretation and significantly enhances the detection and staging performance of bladder cancer imaging.

Biosensors doi: 10.3390/bios16080444

Authors: Hyunwook Ryu Won-Chul Lee Jongwon Lee

Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors from device-level transduction to system-level integration. At the device level, sensing strategies have evolved from direct sensing toward indirect sensing architectures, improving stability and reusability. At the system level, conventional off-chip implementations have progressively shifted toward fully integrated on-chip implementations. Furthermore, this review highlights the emerging paradigm of in-sensor computing, in which sensing, memory, and computation are co-located within a single physical platform. This approach enables reduced data movement and supports energy-efficient operation for point-of-care applications. Finally, key challenges—including CMOS compatibility, device variability, and reliable multi-threshold sensing operation—are discussed as critical factors for the practical realization of memristive-device-based electrochemical biosensing systems.

Biosensors doi: 10.3390/bios16080443

Authors: Yijun Cheng Stella W. Pang

Localized surface plasmon resonance biosensors are promising devices for label-free detection of live cells and biomolecules. However, typical plasmonic sensors have limited surface area, planar electromagnetic fields, and poor compatibility with three-dimensional (3D) interactions with cells or biomolecules. In this study, a 3D plasmonic sensor around microposts was developed using reversal nanoimprint lithography for highly sensitive cell and DNA detection. Au nanopillars were conformally integrated onto the bottom, sidewall, and top of microposts, forming additional sensing surface area along the sidewall of microposts for plasmonic sensing. The 3D plasmonic sensors exhibited tunable resonance peaks and refractive index (RI) sensitivities by varying the micropost height. The highest sensitivity of 1306 nm per RI unit was obtained from the sensor with 10 μm-tall microposts at a resonance wavelength of 1315 nm, which was significantly higher than that of typical planar plasmonic sensors. The platform was applied to live MC3T3-E1 cell detection, showing a resonance peak shift of 71 ± 11.6 nm at a cell concentration of 106 cells/mL with a cell concentration ranging from 102 to 106 cells/mL. In addition, DNA hybridization detection was demonstrated over a concentration range of 10−15–10−7 M complementary target DNA, with a resonance shift of 68 ± 2.5 nm observed at 10−7 M target DNA concentration. The 3D plasmonic sensor provides a scalable device for additional plasmonic biointerfaces with enhanced analyte accessibility and light–matter interactions. This platform offers high-sensitivity biosensing involving live cells, nucleic acids, and other biological targets.

Biosensors doi: 10.3390/bios16080442

Authors: Khulud Salem Alshudukhi Noshina Tariq

Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge–Fog–Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96–99.47% for accuracy and 99.08–99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments.

Biosensors doi: 10.3390/bios16080441

Authors: Marius Danhausen Sebastian Buchinger Shimshon Belkin Thomas Andreas Ternes

We report a freeze-dried ready-to-use yeast thyroid screen (YTS), preserving the general dose–response characteristics of the freshly prepared counterpart. This field-deployable method reduces the assay time of the overall procedure from several days to 5 h with no requirement for sterile conditions, thus fulfilling key requirements for on-site implementation in a biosensor array. The effects of cell density and concentration of the cryoprotectant trehalose on median effective concentrations (EC50), limit of detection (LOD) and biosensor induction (IF) were determined and monitored over a storage period of 5 months. In addition, the impact of these parameters was monitored on the biosensor survival rate during freeze-drying and the subsequent storage process. Throughout the 5-month study, the freeze-dried recombinant yeast assay retained comparable dose–response characteristics to those of the freshly prepared counterpart, displaying median values of EC50 in the range of 350 nM to 550 nM and LODs in the range of 20 nM to 45 nM of the reference compound thyroxine (T4). Long-term stabilization is demonstrated using spiked (T4, 2 µM) river water and extracted wastewater effluent. After 5 months of storage, the T4-equivalent activities were 96 ± 38% and 112 ± 15% for river water and wastewater, respectively. In summary, we have successfully demonstrated a proof of principle of a field-deployable yeast thyroid screen (YTS) by using freeze-dried cells and trehalose as a cryoprotectant to achieve storability for up to 5 months at 4 °C.

Biosensors doi: 10.3390/bios16080440

Authors: Seunghye Yang Jeongwook Lee Om Darlami Dongyun Shin

γ-Hydroxybutyric acid (GHB), a colorless and odorless central nervous system depressant associated with drug-facilitated sexual assault, demands rapid on-site detection. Polydiacetylene (PDA) colorimetric sensors derived from 10,12-pentacosadiynoic acid (PCDA) conjugates are a promising platform, but the molecular origin of GHB recognition in PCDA–gabazine systems has remained unresolved. Here, we compare a series of structurally related PCDA conjugates to examine how the chemical state of the EDA-derived unit affects the GHB-induced colorimetric response. A side-by-side substituent screen of three PCDA derivatives showed that PCDA–EDA produced the largest colorimetric response (ΔR = 53) within 30 s, the hydrazide analogue gave a moderate response (ΔR = 34), and a simple amide was negligible (ΔR = 12). By contrast, a PCDA–gabazine mat prepared by the same protocol showed only a subtle, barely discernible color shift after several hours and remained predominantly blue even after approximately 24 h, without a visually appreciable blue-to-red transition. This difference suggests that the accessible free primary amine of PCDA–EDA is an important factor contributing to its faster and stronger response. Building on this mechanistic finding, we replaced the previously used PVDF–HFP/PEO matrix with a cellulose/PVDF–HFP formulation processed from DMF and adopted multi-nozzle electrospinning, reducing the fabrication time from approximately 120 to 30 min per sheet, corresponding to a 75% reduction in processing time. The sensor mat showed a clearly distinguishable, dose-dependent visual response across 0.5–3% w/v GHB within 30 s, covering the forensically relevant concentration window. These findings reposition linker architecture as a central design parameter for PDA-based forensic colorimetric sensors.

Biosensors doi: 10.3390/bios16080439

Authors: Carlotta Panciera Giuseppe Brunetti Caterina Ciminelli Muhammad A. Butt

A hybrid metasurface absorber (MSA) based on a central-bar-coupled split-disk dimer is proposed and numerically investigated for high-resolution refractive-index sensing in the near-infrared spectral region. The metasurface consists of silicon nitride dielectric resonators integrated with a gold plasmonic layer, enabling strong electromagnetic confinement, enhanced light–matter interaction, and ultra-narrow resonant features within the 1000–1400 nm wavelength range. The optimized structure supports multiple resonant modes under both x- and y-polarized excitation, producing sharp reflection dips with full-width-at-half-maximum values as low as 0.58 nm and quality factors reaching 2007. Refractive-index sensing performance was evaluated by varying the aqueous superstrate refractive index from 1.33 to 1.35, resulting in bulk sensitivities up to 860 nm/RIU under normal incidence. The angular response was further analyzed for incidence angles up to 5°, revealing polarization-dependent resonance splitting and the emergence of additional high-Q resonant branches under oblique excitation. Several angularly induced resonances exhibit narrower linewidths than those observed at normal incidence while preserving high refractive-index sensitivity up to 870 nm/RIU. Electric-field distributions confirm strong field localization near the dielectric boundaries and coupling regions, validating the hybrid resonant mechanism responsible for the enhanced spectral selectivity and sensing performance. The proposed MSA provides a promising platform for compact and ultrasensitive biosensing applications.

Biosensors doi: 10.3390/bios16080438

Authors: Bingxi Feng Jing Wei

Listeria monocytogenes (LM), one of the most virulent foodborne pathogens, poses a serious threat to public health due to its strong environmental adaptability and high pathogenicity. Rapid, sensitive, and real-time detection of LM is of great importance. Chemiresistive gas sensors have attracted enormous attention in LM detection owing to their advantages of low cost, simple structure, fast response, and easy miniaturization, which can achieve indirect detection of LM by recognizing its specific metabolic volatile organic compounds. This review summarizes the recent progress in chemiresistive gas sensors for the detection of LM metabolites. First, the metabolic characteristics of LM and the typical volatile organic compound (3-hydroxy-2-butanone) as its characteristic biomarker are introduced. Then, the performance and sensing mechanisms of different types of chemiresistive gas sensors for LM metabolite detection are summarized and elaborated systematically. The application of chemiresistive gas sensors for the detection of actual samples and the progress in the design of related detection devices are introduced. Finally, the current challenges faced by chemiresistive gas sensors in LM metabolite detection and their future development prospects are discussed. This review provides a comprehensive reference for the research and practical application of chemiresistive gas sensors in Listeria monocytogenes detection.

Biosensors doi: 10.3390/bios16080437

Authors: Ji-Seung Kim Soo-In Choi Han-Jeong Hwang Chang-Hee Han

Electroencephalography (EEG) measured inside or around ears, called ear-EEG, provides a practical measurement modality for daily brain–computer interface (BCI) applications. However, reliable decoding of mental imagery remains challenging due to the limited number of channels, low signal-to-noise ratio (SNR), and substantial inter- and intra-subject variability inherent to ear-EEG. Addressing these constraints requires advanced decoding strategies specifically optimized for this signal domain. In this study, we retrospectively analyze the ear-EEG dataset of a previous study in which a real-time endogenous BCI was evaluated using conventional machine learning. Specifically, we present an offline benchmark of 23 deep neural network architectures originally developed for scalp-EEG, which were adapted to ear-EEG and evaluated under an identical validation framework. To the best of our knowledge, this is the first systematic comparison of this breadth for ear-EEG-based mental-task classification. Beyond conventional performance comparison, we identify the optimal architecture by jointly considering statistical significance and a performance–cost trade-off, incorporating classification accuracy, parameter count, and measured computational cost. Our results demonstrate that FBLightConvNet achieves the highest classification accuracy among all evaluated models and outperforms common spatial pattern-linear discriminant analysis (CSP-LDA), a widely adopted and robust conventional baseline, on all three recording days, with the difference reaching statistical significance on Days 2 and 3. Notably, many state-of-the-art scalp-EEG models fail to generalize effectively to ear-EEG, highlighting the importance of architecture selection in this domain. These findings identify the best-performing architecture in this setting and indicate which architectural characteristics support effective ear-EEG decoding. Ultimately, this study offers practical design insights and a reproducible benchmarking framework for developing lightweight and high-performance deep learning models, which we hope will support future efforts toward real-world ear-EEG-based BCI systems.

Biosensors doi: 10.3390/bios16080436

Authors: Reham Ali Majd K. Almotiri Sabri Messaoudi Ibrahim A. I. Ali Azizah Algreiby Sayed M. Saleh

During this research, a distinctive optical sensor film was devised and designed to detect Cu(II). The 3-acetyl-4-hydroxyquinolin-2(1H)-one (AHQ) sensor probe is effectively synthesized. This organic probe of the sensor film exhibits exceptional sensitivity to Cu(II) ions and a “turn-off” state. This innovative fluorescent chemosensor is distinguished by its unique optical characteristics, which include a significant Stokes shift of approximately 91 nm. The binding of Cu(II) with AHQ organic probe introduces a 1:2 (metal:ligand) complex, accompanied by the quenching of the maximum emission peak at 455. Furthermore, AHQ exhibits exceptional selectivity for Cu(II). The quenching of the complex fluorescence is attributed to internal charge transfer (ICT), as indicated by the mechanism. The AHQ sensing molecule for Cu(II) ions is attributed to chelation-quenched fluorescence. Density functional theory (DFT) and time-dependent DFT (TDDFT) were employed to study the binding of Cu(II)–AHQ structures and related electronic characteristics in solutions. The results reveal that the luminescence quenching of this complex is caused by ICT. The influences of the interference ions were investigated using a solution that contained multiple metal ions. This AHQ molecule exhibits exceptional selectivity and sensitivity and a low LOD of 10.8 nM, and is administered in a physiological pH medium (pH = 7.4) with a relative standard deviation (RSDr) (1%, n = 3). Also, the AHQ shows good binding behaviour towards Cu(II), and the binding constant was determined to be 3.8 × 106 M−1. As a result, these unique characteristics allow it to identify Cu(II) within a controlled dynamic range of 0.019–2.4 μM Cu(II). The reversibility of the chemosensor was established by using EDTA as a strong chelating agent. As a highlight, we present an important optical chemosensor dependent on the AHQ molecule.

Biosensors doi: 10.3390/bios16080435

Authors: Sumeyra Savas Seyed Mohammad Taghi Gharibzahedi

Shigella is a foodborne bacterial pathogen with a low infectious dose and significant public health impact. Culture-based and molecular techniques provide reliable identification but are time-consuming. Nanoparticle-based biosensors offer sensitive, selective, and compact alternatives. Recent advances in nanoparticle-based biosensors for Shigella spp. (S. flexneri, S. sonnei, S. dysenteriae, and S. boydii) detection have been reviewed in terms of signal amplification, biorecognition, biological targets, sensor types, and performance in real food matrices. Detection strategies rely on gene-level and whole-cell recognition. Targeting virulence genes, invasion plasmid antigen H (ipaH), provides stable genus-level identification, whereas whole-cell recognition facilitates rapid detection without extensive sample preparation. Optical biosensors, including fluorescence-based methods, surface-enhanced Raman spectroscopy (SERS), and localized surface plasmon resonance (LSPR), achieve low detection limits with strong tolerance to complex food matrices. Electrochemical biosensors offer operational simplicity, portability, and suitability for food screening. Lateral flow and hybrid systems provide rapid detection through simplified assay formats and visual readout, with performance influenced by the balance between speed and sensitivity. Validation in real food matrices shows acceptable recoveries, minimal cross-reactivity, and agreement with reference methods. This overview provides a design-oriented framework for nanoparticle-based biosensor selection in food safety by integrating nanomaterial function, biosensor design, and performance characteristics.

Biosensors doi: 10.3390/bios16080434

Authors: Deping Huang Xiu Zhang Ye Li Jingfu Wu Youzhi Yue

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in affective brain–computer interface research due to its non-invasive nature, portability, and robustness to motion artifacts. However, substantial inter-subject variability in neural responses remains a major challenge for subject-independent emotion recognition. To address this issue, this work presents an effective integration of multi-scale temporal convolution and dual-attention mechanisms for subject-independent fNIRS emotion recognition evaluated under the leave-one-subject-out protocol within a single dataset. The proposed framework employs multi-scale temporal convolutions to capture hemodynamic characteristics at different temporal resolutions and incorporates channel and temporal attention mechanisms to adaptively emphasize informative brain regions and critical temporal segments. Experiments were conducted on both a self-collected fNIRS emotion dataset and the publicly available ENTER dataset using the Leave-One-Subject-Out (LOSO) evaluation protocol. On the self-collected dataset, MSA-CNN achieved an accuracy of 65.06 ± 7.10% with an F1-score of 0.605. On the ENTER dataset, the proposed model obtained an accuracy of 68.91% and an F1-score of 0.621, outperforming conventional machine learning approaches and several representative deep learning baselines. Ablation studies further demonstrated the positive contributions of both the multi-scale convolutional structure and the dual-attention mechanism. Experimental results on both the self-collected and ENTER datasets demonstrate that the proposed MSA-CNN achieves competitive emotion recognition performance under the LOSO protocol. Class-wise evaluation using precision, recall, and the F1-score further provides a comprehensive assessment of the model’s classification behavior. These results indicate the effectiveness of the proposed framework for cross-subject fNIRS-based emotion recognition under the current experimental settings. The results indicate that multi-scale temporal feature learning combined with attention mechanisms can effectively enhance fNIRS-based emotion recognition performance and provides a promising framework for within-dataset cross-subject evaluation in fNIRS-based emotion recognition. Future work will focus on expanding the subject population, conducting cross-dataset train–test evaluations, and incorporating multimodal neural signals to further improve robustness and generalization.

Biosensors doi: 10.3390/bios16080433

Authors: Dongdong Yin Xinjun Chen Zhixing Cheng Yu Liu Yin Dai Xuehuai Shen Xiaocheng Pan

Goose astrovirus genotype 2 (GAstV-2) is an important pathogen associated with gosling gout, and rapid detection is useful for early diagnosis and field surveillance. In this study, a visual assay for GAstV-2 detection was developed by combining one-step reverse transcription multienzyme isothermal rapid amplification (MIRA) with the nucleic acid cleavage activity of Pyrococcus furiosus Argonaute (PfAgo). MIRA primers and specific guide DNAs were designed based on a conserved region of the GAstV-2 ORF1b gene, and the PfAgo reaction conditions were optimized. The optimal reaction contained 1.0 μM gDNA, 0.6 μM PfAgo, and 1.0 mM MnCl2. Using recombinant pUC57-ORF1b plasmid DNA as the template, the lowest detectable plasmid concentration under the tested conditions was 1.0 × 100 copies/μL. In the specificity assay, only GAstV-2 produced a positive signal, with no cross-reaction observed with GAstV-1, Tembusu virus, H9-subtype avian influenza virus, goose circovirus, fowl adenovirus serotype 4, or goose parvovirus. The assay was further tested with 23 clinical samples suspected of GAstV-2 infection. In a preliminary evaluation of 23 clinical samples, the RT-MIRA-PfAgo results were concordant with those obtained by conventional RT-PCR and RT-qPCR. Overall, the RT-MIRA-PfAgo assay provided sensitive and specific GAstV detection within a short time, without requiring programmed thermal cycling or an expensive real-time PCR instrument for routine endpoint detection. This method may be useful for GAstV-2 detection in basic laboratories and field settings.

Biosensors doi: 10.3390/bios16080432

Authors: Jiyang Zhang Lingjun Liu Xin Yu Xiao Li Zhongyou Li Taoping Bai Wentao Jiang

Hepatic venous pressure gradient (HVPG) is the clinical gold standard for assessing portal hypertension, but its dependence on different vascular resistance sources remains unclear. This study evaluated the respective effects of sinusoidal resistance (SR) and portal venous resistance (PVR) on HVPG using an in vitro hemodynamic platform. A mock circulatory loop with dual hepatic blood supply was constructed and calibrated to near-physiological conditions. SR and PVR were independently adjusted to reproduce different portal hypertension states, and HVPG was measured by balloon wedging. A perturbation index (PI) was introduced to quantify the systemic effect of wedging, and Sobol global sensitivity analysis was used to compare resistance contributions. HVPG increased markedly with SR, ranging from 3.69 to 12.25 mmHg, but showed only limited changes with PVR, ranging from 1.03 to 6.56 mmHg. Sobol analysis confirmed the dominant contribution of SR over PVR (S1: 0.856 vs. 0.034). Balloon wedging also induced measurable systemic perturbations, particularly under high-resistance conditions. Overall, HVPG is highly sensitive to SR but relatively insensitive to PVR, indicating a systematic underestimation risk in presinusoidal portal hypertension. Moreover, the hemodynamic perturbation induced by balloon wedging should not be neglected in severe portal hypertension, suggesting that this effect should be incorporated into virtual HVPG models to improve their predictive accuracy.

Biosensors doi: 10.3390/bios16080431

Authors: Samriddha Dutta Ashok Mulchandani

Carbon nanotube (CNT)-based biosensors have emerged as promising platforms for non-invasive biofluid analysis because of their high electrical conductivity, large surface area, tunable optical properties, and versatile surface chemistry, enabling miniaturized, flexible sensing devices. Sweat, saliva, tears, and urine are increasingly recognized as attractive alternatives to blood for point-of-care diagnostics because they enable repeated, non-invasive sampling while containing clinically relevant metabolites, electrolytes, proteins, hormones, nucleic acids, pathogens, and other biomarkers. However, the low abundance of many analytes, matrix complexity, biofouling, and biofluid-specific variability present significant analytical challenges. This review critically examines the different CNT-based sensor architectures, and their recent advances in non-invasive analysis of sweat, saliva, tears, and urine. It integrates sensor architecture, biofluid-specific analytical challenges, sample-validation level, and translational readiness within a single comparative framework. Representative applications are discussed for metabolic monitoring, renal health assessment, infectious disease testing, and other clinically relevant uses. Beyond clinical diagnostics, emerging non-clinical applications, including drug-of-abuse detection, forensic body-fluid identification, and occupational or environmental exposure assessment, are also highlighted. Finally, we discuss key barriers limiting real-world translation of CNT biosensors, including material reproducibility issues, biofouling, physiological interpretation of biofluid biomarkers, scalable manufacturing, and long-term operational stability, and outline future strategies to advance these platforms toward robust, reliable, and widely deployable biosensing technologies.

Biosensors doi: 10.3390/bios16080430

Authors: Tae-Won Seo Won-Yong Jeon Hyug-Han Kim Young-Bong Choi

Hydrophilic osmium(Os)-based redox polymers were designed as electron-transfer mediators for fungal flavin adenine dinucleotide-dependent glucose dehydrogenase (FAD-GDH)-based glucose sensors. Poly(vinylimidazole-co-hydroxyethyl acrylate) (PVI-HEA) copolymers with different HEA compositions were synthesized and coordinated with Os(dmo-bpy)2Cl2 to prepare PVI-HEA-Os(dmo-bpy)2Cl2 redox mediators. The synthesized mediator systems were characterized using 1H-nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, ultraviolet–visible spectroscopy, field emission scanning electron microscopy/energy dispersive spectroscopy, zeta potential analysis, cyclic voltammetry, and electrochemical impedance spectroscopy. The results confirmed the successful formation of Os redox polymer structures and their immobilization on the electrode surface. The electrochemical behavior and glucose sensing performance strongly depended on the PVI-HEA composition. Among the compositions tested, PVI-HEA(3.5:1)-Os(dmo-bpy)2Cl2 showed the strongest redox current response, stable aqueous dispersion behavior, and relatively low interfacial charge-transfer resistance. Glucose-sensing measurements using FAD-GDH/mediator-modified electrodes showed linear current responses over the glucose concentration range of 1.25–20 mM. The PVI-HEA(3.5:1)-Os(dmo-bpy)2Cl2-based electrode showed the highest sensitivity of 16.18 μA cm−2 mM−1, which was significantly higher than those observed at lower-HEA compositions. The optimized mediator system also showed selective glucose responses against representative biological interferents, including ascorbic acid, uric acid, dopamine, and serotonin. Stable catalytic current responses were maintained under Human Plasma-Like Medium conditions, suggesting improved matrix tolerance compared to conventional PVI-based Os redox polymers. The improved sensing performance was attributed to the hydrophilic polymer environment introduced by the HEA units, which may facilitate favorable interfacial charge-transfer behavior within the enzyme–mediator layer. The results show that the hydrophilic copolymer composition plays an important role in the electrochemical behavior and glucose sensing performance of Os redox polymer mediators. The proposed PVI-HEA-Os(dmo-bpy)2Cl2 system may be a promising candidate for future enzymatic glucose sensing and continuous glucose monitoring-related applications.

Biosensors doi: 10.3390/bios16080429

Authors: Yao Lu Weiguo Liang Jun Qian Junpo Li Shengpeng Wu Mao Xia Haixuan Sun

Rapid hemostasis assessment is essential for managing acute coagulopathy, guiding anticoagulant therapy, and monitoring cardiac surgery patients. Although viscoelastic testing provides valuable guidance for early intervention, its widespread adoption is constrained by large blood usage (~2 mL), prolonged turnaround times (several hours), long assay durations (~25–40 min), and high costs. To overcome these limitations, we developed an innovative, all-in-one magnetoelastic (ME) sensing chip that enables systematic coagulation kinetics monitoring using only 46 μL of whole blood within 15 min. Featuring prepackaged lyophilized reagents and pumpless blood loading, this user-friendly chip is highly cost-effective for disposable use. The experimental results demonstrated good reproducibility for on-chip extrinsic coagulation activation, with clotting parameters maintaining coefficients of variation under 10%. Furthermore, clotting parameters derived from heparin monitoring results exhibited a strong linear correlation that compares favorably with the clinical standard (r = 0.986). Finally, sensitivity evaluation toward various blood components validated the sensor’s dual-mode characterization strategy for identifying coagulation factors and fibrin-related coagulopathies. The proposed ME sensing chip holds promise for bedside testing and flexible, on-demand coagulation monitoring across diverse clinical scenarios.

Biosensors doi: 10.3390/bios16080428

Authors: Li-Ang Lee Li-Pang Chuang Guo-She Lee Cheng-Kuo Lai Huei-Dan Cheng Zi-Xuan Huang Zong-Han Lee Liang-Yu Shyu Hsueh-Yu Li Chi-Hung Liu Yi-Ping Chao

Obstructive sleep apnea syndrome (OSAS) is heavily implicated in subclinical cardiovascular disease; however, traditional polysomnographic metrics fail to capture the localized mechanical trauma exerted on the carotid artery. To address this methodological gap, this study evaluated exploratory associations between frequency-domain snoring characteristics and right carotid artery alterations in 50 patients with OSAS. Snoring was quantified using a dual-modality bioelectronic approach: an ambient microphone captured airborne snoring sound energy (SSE), while a portable neck-surface piezoelectric sensor recorded tissue-conducted snoring vibratory energy (SVE). Subclinical vascular changes, including carotid intima-media thickness (CIMT) and atherosclerosis, were assessed via ultrasonography. Hierarchical multivariable regression demonstrated that acoustic SSE%-404–500 Hz and mechanical SVE%-112–144 Hz independently correlated with preliminary CIMT increases (adjusted β = 0.033 and 0.021, respectively; both p < 0.05), alongside neck circumference. Conversely, SSE%-404–500 Hz emerged as an exploratory marker for focal carotid atherosclerosis (adjusted odds ratio = 1.828; p = 0.009). Integrating this metric with baseline parameters yielded exploratory diagnostic capacity (area under the curve = 0.833; p < 0.001), achieving 89% sensitivity and 69% specificity. These findings suggest that dual-modality spectral analysis provides a non-invasive exploratory framework for cardiovascular risk stratification, isolating localized mechanotransduction phenotypes independently of systemic hypoxia.

Biosensors doi: 10.3390/bios16080427

Authors: Ning Xia Fengli Gao Chuye Zheng

Alzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide. Early diagnosis of AD is crucial for delaying disease progression and improving patients’ quality of life. Blood biomarkers, particularly amyloid-beta (Aβ) and Tau proteins along with their phosphorylated isoforms, show advantages such as convenient sampling, minimal invasiveness, and excellent repeatability. However, the extremely low concentrations of AD biomarkers in blood impose stringent requirements on the sensitivity, specificity, and anti-interference capability of detection methods. Optical methods provide promising analytical platforms to address these challenges in view of their intrinsic merits of high sensitivity and selectivity; rapid response; and potential for miniaturization. This review systematically summarizes the latest advances in optical methods for the detection of the two core AD blood biomarkers (Aβ and Tau), covering techniques such as colorimetry, fluorescence, chemiluminescence, surface plasmon resonance (SPR), and surface-enhanced Raman scattering (SERS). The sensing principles, design strategies, and analytical performances of these methods are discussed, with special emphasis on different signal amplification strategies. In addition, several challenges and future prospects are provided with a primary focus on single-molecule detection, insufficient sensitivity and stability, lack of validation with large clinical cohorts, and absence of standardization. This review aims to provide researchers with guidance for the rational development of high-performance optical methods to achieve early diagnosis of AD.

Biosensors doi: 10.3390/bios16080426

Authors: Fengli Gao Lin Liu Junyue Li Shaoyang Chen Xinyao Yi

Early diagnosis of Alzheimer’s disease (AD) can facilitate the establishment and implementation of therapeutic interventions. The currently used diagnosis methods for AD mainly include cerebrospinal fluid analysis and positron emission tomography imaging. Due to their high invasiveness, high cost, and limited accessibility, these technologies are difficult to meet the needs of large-scale population screening, grading diagnosis, and treatment, thereby limiting the popularization of early diagnosis of AD. The detection of blood biomarkers has become an important breakthrough in early screening and diagnosis of different diseases due to its non-invasive, low-cost, and easy-to-operation advantages. Recently, blood proteins such as amyloid-beta (Aβ), total and phosphorylated Tau, light chain neurofilaments (NFL), and glial fibrillary acidic protein (GFAP) have been considered promising biomarkers for the diagnosis of AD. However, there is currently no effective, minimally invasive, and easily accessible detection method for clinical diagnosis and risk prediction of AD. Electrochemical and electrical biosensors are highly sensitive, simple, fast, and cost-effective analytical tools for disease monitoring, drug development, and target detection. In this work, we comprehensively and systematically overview the progress of various electrochemical and electrical techniques for determining AD-related blood protein biomarkers, mainly including electrochemistry, electrochemiluminescence, photoelectrochemistry, quartz crystal microbalance, field-effect transistor, and organic electrochemical transistor. This work can provide guidance for researchers to develop novel electrochemical and electrical biosensors for early and accurate diagnosis of AD.

Biosensors doi: 10.3390/bios16080425

Authors: Jorge A. Uc-Martín Alejandro Cortés-Díaz-Sandi Ilianny Castellón-Pérez Roberto G. Ramírez-Chavarría

Electrical bioimpedance (BioZ) has emerged as a promising technique for the non-invasive monitoring of physiological parameters, owing to its ability to map functional activity into electrical changes. Particularly, impedance plethysmography (IPG) is used to track blood volume changes associated with cardiac activity. However, developing flexible, low-cost devices with enough sensitivity to serve as high-precision for IPGs remains an open challenge. In this work, we introduce laser-induced graphene (LIG) electrodes as an attractive alternative for IPG measurements. The electrodes were fabricated by generating LIG on a polyimide substrate using a 405 nm laser diode and were subsequently characterized morphologically, structurally, and electrically to produce a wrist-worn cardiac impedance sensor (WCIS). The design of the WCIS is based on interdigitated electrodes to detect IPG variations at the radial artery, from which the heart rate is estimated. We show experimental results on IPG signal analysis and its validation against electrocardiogram (ECG) signals as the gold standard. As a result, a mean absolute error (MAE) of 1.7 bpm, a root mean square error (RMSE) of 2.1 bpm, and a limit of agreement of approximately ±6 bpm were obtained. These outcomes demonstrate the feasibility of the WCIS as a promising, low-cost alternative for continuous, non-invasive cardiovascular monitoring in portable devices, based on the IPG principle.

Biosensors doi: 10.3390/bios16080424

Authors: Ziyi Xiao Ruyi Deng Xin Liu Jiahuan Zheng Kaisong Yuan

Real-time pH measurement and regulation of microliter-scale liquid samples are critical for microfluidic biochemical applications. However, traditional magnetic stirring for macroscopic systems is not applicable under such microscale conditions. This work develops an ultrasound-assisted screen-printed electrode (SPE) pH sensor integrated with a 3D-printed ultrasonic reflection cell and piezoelectric excitation unit. Ultrasound-induced acoustic streaming accelerates mass transfer inside microliter-scale samples. Upon acid addition, the ultrasonic-assisted system reaches potentiometric equilibrium far more rapidly than the static non-ultrasonic control. Optimized parameters are 30 s ultrasonic duration and 5 Vpp input amplitude. The sensor shows linearity from pH 3.04 to 4.15 with R2 = 0.9919. Phenolphthalein visualization directly confirms ultrasound-enhanced molecular diffusion. Its mass transfer efficiency matches traditional magnetic stirring, and stable fast equilibrium is realized in complex cell culture medium. This acoustofluidic micromixing strategy offers a simple integrated route for pH monitoring and the regulation of micro-volume biological samples.

Biosensors doi: 10.3390/bios16080423

Authors: Liu Wang Yang Zhou Wenjia Li Lijuan Shi

Multimodal medical image fusion integrates complementary information from heterogeneous imaging modalities to provide comprehensive visual support for clinical analysis. Most existing methods adopt an “encode–fuse–decode” paradigm that applies a single fusion rule only at the deepest network layer, often discarding shallow detail features and yielding blurred outputs with poor textural fidelity. To address this limitation, we propose MSFusion, a novel hierarchical framework that distributes adaptive fusion throughout the entire decoder stage. By leveraging skip connections to align decoder layers with corresponding encoder features, MSFusion enables full-scale integration of multi-resolution representations. The architecture employs a dual-branch convolutional encoder and introduces two core modules in the decoder: (1) the Multi-Scale Adaptive Fusion (MSAF) module, which addresses insufficient exploitation of cross-scale complementarity by dynamically weighting features via learnable attention, thereby balancing fine details and global semantics, and (2) the Multi-Scale Cross-Modal Cooperative Fusion (MSCMCF) module, which mitigates semantic misalignment through a cross-modal interactive attention mechanism that establishes robust inter-modality correspondences and promotes deep feature alignment. Additionally, a Vision RWKV (VRWKV) block is integrated to efficiently model both local and global spatial dependencies with linear computational complexity. Extensive experiments on public CT–MRI, PET–MRI, and SPECT–MRI datasets are evaluated using six standard metrics. On the CT–MRI benchmark, our method achieves MSE (↓) = 0.0423, CC = 0.8198, and SCD = 0.7023, outperforming state-of-the-art approaches. These results—combined with superior visual quality—demonstrate that MSFusion sets a new standard for accurate, detailed, and clinically meaningful multimodal image fusion.

Biosensors doi: 10.3390/bios16080422

Authors: Meruyert Amanzholova Ainur Akimbekova Aisha Shaizadinova Nazgul Sutimbekova Nelya Bissenova Pavel Tarlykov Sailau Abeldenov

Acinetobacter baumannii is a critical WHO priority pathogen due to its multidrug resistance and high mortality in carbapenem-resistant infections. Resistance is predominantly mediated by class D carbapenemase genes blaOXA-23 and blaOXA-40, which spread rapidly via horizontal gene transfer in healthcare settings. To address the lack of a rapid assay capable of detecting both blaOXA-23 and blaOXA-40 in a single analytical workflow, we developed a multiplex two-step RPA–CRISPR/Cas12a assay. Since infections caused by strains harboring either gene require identical therapeutic management, their co-detection in a single reaction is clinically justified. Although simultaneous use of two crRNAs within a single CRISPR/Cas12a reaction is often considered technically challenging due to potential inter-crRNA competition, here it advantageously enables dual-target coverage without compromising sensitivity. The assay demonstrated high specificity with no cross-reactivity against a panel of clinically relevant bacterial species, including closely related Acinetobacter spp. Evaluation using genomic DNA extracted from 63 cultured clinical A. baumannii isolates revealed blaOXA-23 in 19 isolates (30.2%), blaOXA-40 in 28 (44.4%), and co-carriage of both genes in 9 (14.3%), with at least one resistance gene detected in 60.3% of isolates. The complete workflow was accomplished within 45 min without specialized equipment, offering a rapid, sensitive, and cost-effective solution for point-of-care molecular surveillance of carbapenem-resistant A. baumannii in clinical and resource-limited settings.

Biosensors doi: 10.3390/bios16080421

Authors: Ran Zhang Meiyu Zhong Caiyun Ma Zhijun Xiao Yuwei Zhang Chengyu Liu

Electroencephalography (EEG)-based emotion recognition is an important biosensing technique for affective brain-computer interfaces (BCIs), mental-state assessment, and physiological monitoring. Existing methods often rely on a single spectral descriptor or regular two-dimensional brain maps, which makes it difficult to jointly model local spatial representations, global spatial relations, and temporal dynamics. This paper proposes GLSTNet, a global-local spatial relations and temporal dynamics network for EEG emotion recognition. EEG trials are divided into short windows, from which multi-band spectral features are extracted and arranged into compact spatial maps. The local spatial encoder (LSE) learns local spatial and spatial–spectral representations from these compact multi-band spatial maps. The global spatial-relation encoder (GSRE) models long-range spatial relations between non-adjacent electrodes using a Pearson correlation prior and a learnable residual adjacency matrix. After local and global representations are integrated through gated fusion, the temporal dynamics encoder (TDE) models consecutive EEG windows using a gated recurrent unit with temporal attention. Comprehensive validation is conducted on two public EEG emotion datasets, the Database for Emotion Analysis using Physiological Signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). In the subject-dependence setting, GLSTNet achieves 93.50 ± 3.22% accuracy for valence and 93.79 ± 3.64% accuracy for arousal on DEAP, and 92.48 ± 3.30% accuracy on SEED. In the subject-independence setting with target-subject calibration, GLSTNet obtains 75.61 ± 6.19% and 79.57 ± 5.99% accuracy for DEAP valence and arousal, respectively, and 88.22 ± 4.70% accuracy on SEED. These results indicate that integrating global-local spatial relations with temporal dynamics provides an effective representation strategy for EEG-based emotion recognition.

Biosensors doi: 10.3390/bios16080420

Authors: Yiren Yin Yujie Yi Yazheng Yu Tatyana Aleksandrovna Khrustaleva Linlin Zhai Jianhai Yu Wei Zhao Chenguang Shen

Virus-specific antibody responses, including binding antibodies and neutralizing antibodies (nAbs), are important indicators of antiviral immune status after infection or immunization. They provide complementary information on antiviral humoral immunity after infection or vaccination. Antigen-binding antibodies indicate previous exposure and the magnitude of the immune response, whereas receptor-blocking and functional neutralization assays assess whether antibodies interfere with viral entry or infection. Conventional neutralization assays, such as plaque reduction neutralization tests and pseudovirus neutralization tests, provide functional information but are labor-intensive, time-consuming, biosafety-restricted, and difficult to deploy for large-scale or decentralized monitoring. Paper-based biosensors, including lateral flow assays (LFAs), microfluidic paper-based analytical devices (μPADs), and paper-based ELISA, have emerged as promising point-of-care tools owing to their low cost, portability, simple operation, and compatibility with visual or digital readouts. This review critically evaluates these platforms according to whether they measure antigen-binding antibodies, receptor-blocking activity, surrogate neutralization, or functional neutralization and summarizes the applications of these three platforms for monitoring antibody responses against SARS-CoV-2, influenza, dengue, Zika, and monkeypox viruses. Unlike previous reviews that mainly focus on general paper-based biosensor design or conventional nAb assays, this review emphasizes the distinction between antigen-binding, receptor-blocking, and surrogate neutralization readouts, and critically discusses how paper-based signals should be interpreted in relation to functional immunity. We further analyze key translational challenges, including quantitative accuracy, antigen cross-reactivity, standardization, clinical validation, regulatory positioning, and real-world implementation. Future development should combine multiplex detection, standardized calibration, digital and AI-assisted interpretation, and clinically validated assay formats. Paper-based biosensors have considerable potential for decentralized antibody monitoring and public health surveillance, but their clinical utility depends on clear assay positioning and validation against appropriate functional or reference methods.

Biosensors doi: 10.3390/bios16080419

Authors: Shanshan Zheng Hui Wang Zhixue Yu Ruipeng Chen Liang Yang Benhai Xiong Xiangfang Tang

Precise quantitative monitoring of 17β-estradiol (E2) is important for reproductive management in precision livestock farming. However, E2 determination in complex biological matrices remains challenging because of matrix-derived background and signal variability. Here, we developed a nanoconfinement-assisted solid-state ratiometric fluorescent aptasensor integrating target-induced strand displacement (TISD), magnetic separation, and anodic aluminum oxide (AAO) nanochannel confinement. The sensing probe consisted of streptavidin-coated magnetic nanoparticles (MNPs) carrying a FAM-labeled cDNA internal reference and a Texas Red-labeled E2 aptamer reporter. E2 binding promoted dissociation of the Texas Red-labeled aptamer from the magnetic probe. Magnetic separation and washing reduced soluble matrix-derived interference, while subsequent deposition of the sensing complexes onto an AAO membrane mitigated coffee-ring-associated nonuniformity and produced a more spatially uniform dual-color fluorescence distribution for ratiometric analysis. Under matrix-matched calibration conditions, linear ranges of 5.0–50.0 pM were obtained in tap water and sow saliva, 5.0–40.0 pM in whole milk, and 5.0–15.0 pM in post-estrus sow urine. The LOD determined in tap water was 3.62 pM. The different calibration slopes obtained among the four matrices indicated that residual matrix-dependent effects remained and that matrix-specific calibration was required for quantitative analysis. Matrix-matched spike recoveries ranged from 86.92% to 119.54% across the investigated matrices. The aptasensor exhibited the strongest response toward 17β-E2 among the tested compounds; however, cross-reactivities of 77.3% for E3 and 47.3% for 17α-E2 indicated preferential rather than exclusive recognition. Molecular docking suggested a putative binding pose but did not experimentally establish the molecular recognition mechanism. Overall, the platform demonstrated laboratory-scale analytical feasibility in pretreated tap water, sow saliva, whole milk, and post-estrus sow urine. Further development of sample preparation, magnetic handling, membrane loading, probe selectivity, and portable fluorescence readout will be required before in situ or on-site application.

Biosensors doi: 10.3390/bios16080418

Authors: Ting Jou Ding Liyuan Wang Tianyi Chen Tao Yu Ching-Jung Chen Jen-Tsai Liu

Early screening is essential for improving the prognosis of colorectal cancer (CRC), for which fecal occult blood testing (FOBT) remains one of the most widely adopted noninvasive screening approaches. However, conventional FOBT methods often exhibit insufficient sensitivity and limited reliability when handling complex fecal matrices, largely due to nonspecific fouling and cumbersome sample pretreatment procedures. Herein, we report a surface plasmon resonance biosensor integrating a self-assembled anti-fouling interface with DNA aptamer-mediated molecular recognition for sensitive hemoglobin detection in fecal samples. The engineered sensing surface exhibits high interfacial stability and resistance to nonspecific adsorption, enabling direct analysis of fecal samples without complicated pretreatment. The proposed platform achieved a detection limit of 1 nM and a diagnostic accuracy of 97.6%. This study demonstrates the feasibility of SPR-based hemoglobin detection in complex fecal samples using an antifouling sensing interface and highlights its potential as a promising optical biosensing strategy for noninvasive colorectal cancer screening.

Biosensors doi: 10.3390/bios16080417

Authors: Weichen Hou Xiangyang Li Jie Li Kai Dong Wen Zhao Zhaozhong Zeng Jian He Longjiao Zhu Wentao Xu

Staphylococcus aureus (S. aureus) is a common foodborne pathogen that can cause the severe contamination of dairy products. Therefore, there is an urgent need for rapid detection methods. In this study, a paper-based biosensor integrating a dual-recognition strategy using aptamers and antibodies was developed for the sensitive, rapid, and on-site detection of S. aureus in complex milk matrices. The biosensor combines a milk matrix-adapted aptamer with polyclonal antibodies (pAbs) and utilizes colloidal gold nanoparticles (AuNPs) as visual signal reporters. Using a SELEX process tailored to the milk matrix, the high-affinity aptamer SA2-1 was selected to specifically bind S. aureus, while pAbs enabled multi-epitope capture on the paper substrate. The aptamer–AuNP conjugates generated visual signals, achieving a detection limit of 102 CFU/mL within 15 min without an instrument. This dual-recognition strategy synergistically enhances both sensitivity and specificity, offering a cost-effective solution for dairy safety monitoring.

Biosensors doi: 10.3390/bios16080416

Authors: Jin-Ha Choi

A biosensor converts a biological recognition event into a measurable signal, but its analytical performance is determined as much by the material interface as by the recognition element itself [...]

Biosensors doi: 10.3390/bios16080415

Authors: Zuoping Lv Xiaoru Tan Peilong Chen Mingshuo Qu Weijin Guo Jihong Huang Xin Cui

Foodborne infections and enterotoxin poisoning caused by S. aureus pose serious threats to food safety and public health. However, conventional detection methods are often time-consuming, technically complex, and typically require relatively large sample volumes. Herein, we report an aptamer-functionalized rough gold nanoparticle (RPG)-based photothermal platform that enables rapid and quantitative detection of S. aureus using a smartphone-compatible thermal imaging system in combination with a laser and an infrared thermal camera. RPGs were synthesized via a facile one-step method, and exhibited uniform particle size and a measured photothermal conversion efficiency of ~73.20%. After optimization of the aptamer concentration, laser irradiation time, and probe concentration, the proposed method exhibited a wide detection range from 1.0 × 102 to 1.0 × 107 cfu/mL, with an operational detection threshold of 100 CFU/mL under the optimized experimental conditions, a total assay time of less than 40 min, and a sample requirement of only 30 μL per assay, while maintaining acceptable reproducibility. Spike-and-recovery experiments in real samples, including orange juice, tap water, and milk, yielded recoveries of 91.8–105.5%, indicating suitable analytical performance in complex matrices. This work provides a rapid, portable, and low-sample-volume detection strategy for S. aureus, offering a practical approach for on-site food safety monitoring.

Biosensors doi: 10.3390/bios16080414

Authors: Mahdi Poursaberi Guillermo Hauke S. Jamaleddin Mousavi Mohamed H. Doweidar

Microfluidic cell culture systems provide controlled microscale environments for biomedical research; however, cell viability within closed microchambers depends on adequate oxygen availability during the adhesion phase and on the mechanical stresses generated after perfusion begins. Experimental characterization of oxygen depletion and local shear stress remains challenging due to the small dimensions involved and the complexity of transport phenomena. In this study, a computational framework was developed to assess oxygen transport and hydrodynamic shear stress in an SU-8-based microfluidic culture chamber. Oxygen diffusion and cellular consumption were first modeled under static conditions to determine cell survival time prior to perfusion. The influence of chamber height on oxygen availability was investigated, and an empirical correlation was derived to predict oxygen concentration as a function of chamber geometry. Subsequently, medium perfusion was introduced, and the resulting wall shear stresses acting on adhered cells were evaluated under different flow conditions. The simulations demonstrated that chamber height significantly affects oxygen depletion time, while both chamber geometry and flow rate influence the magnitude of wall shear stress. The proposed framework provides practical design guidelines for optimizing microfluidic culture systems, enabling adequate oxygen supply and physiologically compatible mechanical conditions while reducing reliance on extensive experimental testing.

Biosensors doi: 10.3390/bios16080413

Authors: Yujie Sheng Wei Chen Ziyi Zhang Ziyi Cui Peiju Zhong Yu Xia Zihan Yang

Cancer is the second leading cause of death worldwide, and tumor heterogeneity remains a major obstacle to effective therapy. In vitro reconstruction of the tumor microenvironment (TME) is particularly challenging because of its complexity, dynamic nature, and spatial heterogeneity, which limits the predictive value of conventional models for anticancer drug evaluation. Traditional Chinese medicine (TCM) has attracted increasing attention in cancer therapy owing to its multi-component, multi-target, and multi-pathway therapeutic characteristics. However, the complexity of TCM formulations and the diversity of their bioactive constituents make their pharmacological mechanisms difficult to elucidate using conventional experimental models. Microfluidic tumor microenvironment-on-a-chip (TME-on-a-chip) platforms integrate engineered cell culture with dynamic perfusion systems to recapitulate key structural, biochemical, and cellular features of the TME, thus providing a more physiologically relevant platform for anticancer research. With advantages such as low sample consumption, precise spatiotemporal control, multicellular co-culture, and real-time monitoring, these platforms provide a promising strategy for evaluating the efficacy and microenvironment-dependent effects of TCM-derived compounds and formulations. In this review, we summarize recent advances in the construction of TME-on-a-chip models, including multicellular organization, extracellular matrix simulation, vascularization, and immune microenvironment reconstruction, and discuss how these systems can be applied to key questions in TCM-based anticancer research. We further analyze current technical and methodological challenges that limit their broader adoption in TCM research and highlight future directions for promoting mechanism-driven and precision-oriented development of TCM in cancer therapy.

Biosensors doi: 10.3390/bios16080412

Authors: Dario Bovio Alice Turrini Alice Prandoni Alberto Porro Pietro Cerveri Caterina Salito

Arterial blood pressure (BP) is a crucial physiological parameter reflecting cardiovascular function and exhibiting dynamic oscillations in response to physiological, psychological, and environmental changes. Since conventional cuff-based measurements provide only intermittent “snapshot” readings, there is growing interest in reliable cuffless and noninvasive systems capable of capturing BP dynamics over time. This work proposes a continuous cuffless BP estimation system based on SOUNDI®, a proprietary wearable device for multimodal physiological and environmental monitoring developed by Biocubica srl. An end-to-end neural network architecture combining convolutional and long short-term memory (LSTM) layers was designed to automatically extract features from pre-processed signals and directly predict systolic and diastolic BP (SBP and DBP). Data were collected from 20 healthy subjects (7 females, 13 males) using a tailored acquisition protocol with reference targets acquired through the validated GIMA® ABPM50 device. Model performance was evaluated using both a Leave-One-Subject-Out (LOSO) cross-validation framework to assess subject-independent generalizability and a conventional 80/20 segment-level split reflecting a personalized monitoring setup. Under the subject-independent LOSO cross-validation, the model achieved a best-subject MAE of 4.93 mmHg for SBP and 3.76 mmHg for DBP, demonstrating the feasibility of generalizing across unseen individuals despite inter-subject physiological variability. Under the conventional 80/20 split, the framework achieved an MAE of 2.98 mmHg for SBP and 2.68 mmHg for DBP, with RMSE values of 4.16 mmHg and 3.72 mmHg, respectively, and Pearson correlation coefficients of R = 0.95 for SBP and R = 0.94 for DBP. These results establish a promising feasibility proof-of-concept for multimodal cuffless BP tracking under dynamic states and nocturnal conditions, highlighting both the strength of multi-sensor fusion and the value of lightweight calibration for real-world deployment on larger, clinical cohorts.

Biosensors doi: 10.3390/bios16080411

Authors: Zengxiang Wang Wenfei Mo Zhaoyang Wang Cuizhen Sun Xia Xiao Xiaojun Huang

Microwave resonance sensors are promising for dielectric characterization and biochemical detection. However, their sensing performance is constrained by the relatively long wavelength at microwave frequencies and the limited electromagnetic interaction with small-volume samples. In this paper, an exceptional-point (EP)-enhanced chiral spoof localized surface plasmon (SLSP) resonator is proposed for high sensitivity microwave biosensing with sub-microliter sample volumes. By rotating the chiral resonator relative to the microstrip feeding line, two EPs are obtained at α = 49° and α = 210°. The simulated results show that the EP states generate larger frequency splitting than the reference state under both dielectric-constant variation and detection-limit evaluation, confirming the squareroot response to weak perturbations. Experiments further validate the sensing performance using low-loss dielectric materials and high-loss glucose solutions. For dielectric samples with relative permittivities from 2 to 6.15, the frequency splitting increases from 0.287 GHz to 0.359 GHz. For glucose solutions, the frequency splitting decreases from 1.506 GHz to 1.372 GHz as the glucose amount increases from 2.78 nmol to 27.8 nmol. The proposed sensor requires only about 0.5 μL of sample volume and shows higher sensitivity than reported microwave glucose sensors. These results demonstrate that the EP-enhanced chiral SLSP resonator provides a compact and sensitive platform for trace-volume biochemical detection and integrated microwave biosensing.

Biosensors doi: 10.3390/bios16080410

Authors: S. Fouziya Sulthana U. Mohammed Iqbal B. S. Sreeja Rajesh Anbazhagan

The integration of biochemical and biophysical sensing technologies in wearable devices represents a significant advancement in continuous and individualized health monitoring. Traditional wearable sensors are often restricted to either biophysical signals like mobility, temperature, electrophysiological activity, etc., or electrochemical analytes like glucose, lactate, etc. Integrating these complementary modalities into a single, compact, skin-conformal device creates opportunities for multimodal diagnostics and health monitoring systems. This review summarizes the recent advancements in multifunctional integrated flexible wearable biosensors that monitor chemical and physical parameters simultaneously. Initially, the insights are provided on the advances in flexible materials and fabrication techniques that support next-generation wearable devices. Subsequently, the recent progress in biophysical sensing, biochemical sensing, and strategies for their integration into multifunctional wearable platforms is critically examined. Finally, the review concludes with the key challenges to clinical translation and a perspective on future directions to achieve the next generation of integrated flexible wearable systems.

Biosensors doi: 10.3390/bios16080409

Authors: Fueangfakan Chutrakulwong Mana Intarasawang Kheamrutai Thamaphat

This work is an extension of our previous work on UV-assisted green synthesis of silver nanoparticles (AgNPs) from water hyacinth leaf extract, which is focused on using their unique biogenic capping layer to mitigate matrix interference in clinical diagnostics. The synthesized AgNPs were evaluated for uric acid (UA) detection and used to fabricate a plasmonic colorimetric biosensing platform. The results show that the biosynthesized AgNPs effectively act as optical signal transducers in an uricase-based enzymatic system, in which the natural capping shield provides excellent electrosteric protection, providing excellent colloidal stability without non-specific aggregation in complex matrices. A linear correlation between absorbance and UA concentration was observed in the range of 100–500 µM (R2 = 0.99). The limit of detection (LOD) calculated by the 3σ/slope approach was 25.89 µM. The sensor showed good repeatability with a relative standard deviation (RSD) below 5%, and stability studies showed that the AgNPs retained more than 90% of their initial response after 14 days. Recovery studies in spiked human serum showed satisfactory accuracy (96.3–103.4%), confirming a high tolerance to endogenous interfering species (e.g., ascorbic acid and glutathione) without pre-purification steps. Importantly, the working range covers clinically relevant uric acid concentrations found in human serum. The obtained results point to the potential of waste-derived green-stabilized AgNPs as a robust plasmonic-based colorimetric biosensing platform for the clinical monitoring of uric acid, successfully combining ecological valorization with high-performance, matrix-tolerant diagnostics.

Biosensors doi: 10.3390/bios16080408

Authors: Umm E. Habiba Nida Mateen Keum-Shik Hong Chang-Seok Kim Jing Meng Hwidon Lee Jeesu Kim

Functional near-infrared spectroscopy (fNIRS) is a portable, non-invasive tool for studying cortical function in children with neurodevelopmental and neurological disorders. Although fNIRS use is increasing, heterogeneous study paradigms and cortical targets have limited cross-condition comparisons and the identification of shared research priorities. This systematic review maps cortical regions and reporting patterns in five key conditions—autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), cerebral palsy (CP), hypoxic–ischemic encephalopathy (HIE), and epilepsy (Ep)—from January 2015 to December 2025. A systematic search across five databases identified 72 relevant studies meeting PRISMA 2020 criteria, revealing both similarities and differences across disorders. The prefrontal cortex (PFC) was studied in all five conditions (5/5: 100%), making it the most consistently investigated cortical region. The parietal and temporal cortices were studied in 4 of 5 conditions (80%). The frontal cortex had the most regions investigated, while the temporal cortex showed the most consistent coverage across conditions (2.25 conditions per region). Beyond regional preferences, condition-specific patterns were aligned with their disorder phenotypes: social brain networks in ASD, prefrontal executive systems in ADHD, sensorimotor changes in CP, cerebrovascular monitoring in HIE, and state-dependent changes in Ep. Across conditions, researchers found altered prefrontal activity, disrupted connectivity, and compensatory brain responses, supporting broader frameworks. Collectively, these findings identify the PFC as a shared target for transdiagnostic fNIRS investigations while highlighting important gaps in regional coverage and methodological consistency. Despite methodological differences, fNIRS shows promise for identifying both shared and unique brain patterns in pediatric neurodevelopmental and neurological disorders. This review provides a framework for prioritizing cortical targets and guiding future standardized fNIRS research in pediatric populations.

Biosensors doi: 10.3390/bios16080407

Authors: Aqsa Imran Muhammad Babar Ramzan Laraib Hashmi Sheheryar Mohsin Qureshi Maham Raza Shahood uz Zaman

Diabetes management depends on regular glucose monitoring, yet conventional blood-based methods are invasive and can reduce user comfort. This review presents an overview of wearable glucose monitoring technologies, with emphasis on non-invasive approaches. It first distinguishes invasive, minimally invasive, and non-invasive monitoring and discusses the use of interstitial fluid, sweat, saliva, tears, urine, and breath as alternative sensing media. The review then summarizes optical, electrochemical, electrical/electromagnetic, and nanotechnology-enabled sensing methods, together with representative wearable and commercially reported devices. At the end, textile-based systems are compared with non-textile platforms in terms of comfort, flexibility, signal reliability, durability, and practical integration. Across these approaches, major limitations include variable relationships between alternative biofluids and blood glucose, interference from physiological and environmental factors, calibration requirements, motion artifacts, limited durability, and insufficient clinical validation. Future development requires more reliable sensing, improved wearable integration, standardized testing, and validation under real-world conditions.

Biosensors doi: 10.3390/bios16080406

Authors: Hebah Altaweel Jamal Abu-Ashour Borhan Aldeen Albiss Bassim Abbassi

Effective management of wastewater treatment plants often requires real-time measurements of Biochemical Oxygen Demand (BOD). Conventional methods for determining Biochemical Oxygen Demand (BOD) are often time-consuming, labor-intensive and prone to inaccuracies. Microbial Fuel Cells (MFCs) have emerged as a viable alternative technology for BOD measurement, offering real-time monitoring capability. However, challenges remain in its validity for testing different types of wastewater. This study developed a cost-effective dual-chamber MFC with graphite felt electrodes and a CMI-7000 membrane, inoculated with a microbial consortia grown from anaerobic sludge at optimal conditions (35 °C, pH 7, 1000 Ω external resistance). After one month of biofilm formation, the MFC produced 600 mV. Voltage outputs were measured at six BOD5 concentrations (36 to 583 mg/L) in synthetic wastewater, showing a strong linear correlation between BOD5 concentrations and voltage outputs. The MFC was also tested with five domestic wastewater samples with BOD5 values ranging between 81 and 405 mg/L. The output voltages were inserted into the derived voltage–BOD correlation to obtain BOD5 values within 2.5% to 11% of conventional laboratory results. These findings confirm the potential of MFC-based biosensors as an efficient and accurate tool for real-time wastewater monitoring.

Biosensors doi: 10.3390/bios16080405

Authors: Guijun Shu Fangning Zhang Chuang Yang Hongyu Jia Xiao Wang Ming Yin

High-channel-count neural recording requires wireless links with high throughput, low power, and compact implementation, yet commercial protocols and phase-locked loop (PLL)-based transmitters often trade data rate against power and complexity. We present a low-power, PLL-less wideband on–off keying (OOK) neural-signal transmitter fabricated in a 180 nm CMOS process. The transmitter employs a free-running inductor–capacitor voltage-controlled oscillator (LC-VCO), a Gilbert mixer for OOK modulation and reverse isolation, and a current-reuse stacked power amplifier. It consumes 8 mA from a 3.3 V supply (26.4 mW), demonstrates modulation and receiver frame acquisition at a maximum raw input rate of 90 Mbps, corresponding to a 180 Mbps Manchester-coded line rate, and tunes from 3.266 to 3.445 GHz. End-to-end bit error rate (BER) was measured at raw rates of 15 and 31.2 Mbps, corresponding to encoded rates of 30 and 62.4 Mbps; the latter matches the in vivo data stream. The transmitter was integrated with a 128-channel recording chip and evaluated in freely moving adult C57 mice. Wireless hippocampal spike and local field potential (LFP) recordings, wired-system comparison, and event-locked LFP analysis support its feasibility for untethered neural recording.

Biosensors doi: 10.3390/bios16080404

Authors: Xin Yan Liwei Jiang Xiaolong Zhang Yizhe Zhao Zixin Wan Jun Ma Chunhui Song Yikai Wang Xingliang Liu

Etomidate is an ultrashort-acting imidazole-derived intravenous anesthetic. The non-medical abuse of etomidate as a new psychoactive substance has emerged as a critical public health challenge globally, particularly among adolescents, necessitating sensitive, rapid detection methods for forensic analysis. This study developed a quantum dot-based immunochromatographic test strip and a corresponding rapid detection method for the sensitive detection of etomidate acid in urine. CdSe/ZnS fluorescent quantum dot microspheres were used as labeling probes combined with specific monoclonal antibodies, and the detection protocol utilized a portable fluorescence reader. The method’s validation demonstrated a linear range of 10–2000 ng/mL (y = −0.231lnx + 0.5241, r = −0.991), with intra-batch and inter-batch coefficients of variation below 12%. The spike recovery rates ranged from 88.24% to 95.69%. Cross-reactivity testing against 12 common drugs and structural analogs showed no interference. The detection limit of 10 ng/mL represents a substantial improvement over conventional colloidal gold strips and approaches LC-MS/MS sensitivity. Assays of real urine specimens confirmed that the immunochromatographic test strip and detection method possess favorable accuracy and reliability. This fluorescent quantum dot-based immunochromatographic assay provides rapid, quantitative, and highly specific etomidate screening suitable for on-site forensic applications, effectively bridging front-end rapid testing with confirmatory laboratory analysis.

Biosensors doi: 10.3390/bios16080403

Authors: Hongzhou Chen Mengyu Li Weichao Wu Yang Liu Chuanfu Lu Qi Li Yuwei Ren Yingwang Ye Baocai Xu Kezhou Cai

Staphylococcus aureus (S. aureus) is a significant pathogen that causes foodborne diseases. Meat, with its abundant nutrients and high water activity, constitutes an ideal niche for S. aureus colonization. Numerous studies have shown that human digestive tract diseases caused by consuming meat products contaminated with S. aureus occur frequently. It is of great significance to strictly monitor S. aureus in meat matrices. A novel biosensor employing dual-signal output was developed through the combination of efficient magnetic separation and dual-modal precise detection. Designed for the rapid enrichment of S. aureus, it significantly boosts detection sensitivity and accuracy, thereby enabling the earlier identification of potential contamination sources. This method uses vancomycin-modified magnetic beads as the capture element, and aptamer-modified nanozymes as the signal element. After magnetic separation, the 3,3′,5,5′-tetramethylbenzidine color reaction can be used to quickly and sensitively detect S. aureus. It achieved a detection limit as low as 10 cfu/mL. Moreover, this dual-signal sensor based on efficient magnetic separation can sensitively detect S. aureus in meat products, thus showing good application prospects in food matrices and further improving the reliability and specificity of detection.

Biosensors doi: 10.3390/bios16080402

Authors: Xiao-Xin Liang Haochen Wu Yong Wang

Tear analysis plays a crucial role in the early screening and diagnosis of ophthalmic diseases. However, conventional methods are often limited by poor real-time performance, low portability, and insufficient capability for multi-parameter detection. Here, we present a bioinspired triboelectric electronic tongue integrated with a microfluidic chip for multimodal detection of tear pH and disease-related biomarkers. The system combines three triboelectric nanogenerator (TENG) modes, including droplet-based, dual-electrode sliding, and single-electrode sliding configurations. The droplet-based TENG converts gravitational potential energy into electrical energy, generating a maximum output voltage of 65 V. The sliding TENG further expands the sensing dimensions by characterizing droplet flow behavior and viscosity-related properties. Benefiting from the high sensitivity of the dual-electrode mode and the waveform differentiation capability of the single-electrode mode, the platform enables enhanced sample discrimination. After optimizing key parameters, including droplet height and chip inclination angle, the output stability errors for all three TENG modes were maintained within ±10%. Combined with a random forest algorithm, the multimodal sensing system achieved a classification accuracy exceeding 96.6% for artificial tears with different pH values. Moreover, distinct electrical response patterns were observed for ophthalmic disease-related biomarkers, including Lysozyme, Interleukin-6 (IL-6), and Chlamydia, demonstrating excellent type identification and concentration detection capability. This work provides a self-powered and miniaturized strategy for intelligent tear analysis and multiple-parameter sensing, offering significant potential for ophthalmic disease diagnosis.

Biosensors doi: 10.3390/bios16080401

Authors: Arghya Sett

In recent years, nanopore technology has enhanced analyte detection, enabled higher resolution and achieved single-molecule sensing capability. Aptamer-conjugated nanopore sensing technology combines the high specificity of aptamers with the single-molecule resolution of nanopores. By anchoring aptamers to biological, solid-state or hybrid nanopores, target binding events produce distinct electrical signatures that allow sensitive and label-free detection. This approach enables real-time monitoring of small molecules, proteins, and even pathogens, with promising applications in diagnostics, drug screening, environmental monitoring, etc. Hybrid biological/solid state devices produce robust signals and are suitable for PoC applications. The aptamers “magic bullets” have also been exploited to develop single-molecule antigen detection using nanopores, which offers a promising alternative for accurate virus testing to contain their transmission. Chemical conjugation of aptamers to nanopore interfaces improves selectivity for peptides/amino acids and expands robustness for practical samples. Aptamer-based nanopipettes offer high analytical precision by enabling label-free, real-time detection of target molecules in ultra-small sample volumes. This review maps aptamer–nanopore integration across biological, solid-state, and hybrid platforms. It also explores various types of aptamers integrated into nanopore platforms that cater to precise, single-molecule recognition, paving the way for highly sensitive, portable diagnostics and next-generation therapeutic monitoring tools.

Biosensors doi: 10.3390/bios16080400

Authors: Hazal Su Bıçakcı Yeşilkaya Mete Özbaltan Nihan Özbaltan Cihat Şeker Bartu Yeşilkaya Bengisu Yalçınkaya

Physiological signal analysis using wearable biosensors like an electroencephalogram (EEG) and an electrocardiogram (ECG) is widely investigated for affective computing; however, the integration of deep learning-based affective computing within 6G-driven IoT healthcare infrastructures remains limited, with data transmission latency posing a significant challenge. This study proposes a framework for EEG and ECG-based affective state analysis over 6G IoT networks. We utilize an attention-based deep learning model for three-class emotion recognition from EEG signals, and a ResNet50-based convolutional neural network for three-class stress/affective state classification using ECG data. The framework’s communication performance is evaluated through ray tracing simulations in a virtual hospital environment at 7 GHz and 92 GHz bands. Experimental results on SEED and WESAD datasets demonstrate that the EEG model achieved 90.93% classification accuracy, while the ECG model yielded 82.26% validation and 65.64% test accuracy. Wireless analysis showed RMS delay spread values of 6.53 ns (7 GHz) and 3.32 ns (92 GHz), with correlation bandwidths of 30.63 MHz and 60.24 MHz, respectively. These findings demonstrate the feasibility of integrating wearable biosensor-based affective state analysis with 6G-oriented IoT healthcare communication frameworks, providing a robust foundation for future personalized health and human state monitoring applications.

Biosensors doi: 10.3390/bios16080399

Authors: Miroslav Pohanka

Melatonin is a hormone that plays a role in the regulation of circadian rhythms and is also recognized for its antioxidant and cytoprotective functions. Its determination in biological samples is analytically challenging because the physiological concentration is very low, especially in saliva. Here, a competitive immunoassay based on a quartz crystal microbalance (QCM) biosensor was developed for melatonin determination. The biosensor employed a surface modified with a melatonin–albumin conjugate and a multilayer antibody amplification scheme to enhance the piezoelectric response. The assay was calibrated in the low picogram per liter range and provided a limit of detection of 0.55 pg/mL (2.37 pmol/mL) for a 25 µL sample. The analytical performance was sufficient for the determination of physiological melatonin concentrations in saliva and blood. The assay showed satisfactory selectivity against selected potential interferents and correlated strongly with a standard ELISA method, with a coefficient of determination of r2 = 0.994. Application to authentic human saliva samples confirmed the practical utility of the method, and the prepared biosensors remained stable for at least 140 days. The proposed QCM immunoassay represents a simple and sensitive alternative to conventional melatonin assays and demonstrates the potential of piezoelectric biosensing for non-invasive hormone monitoring.

Biosensors doi: 10.3390/bios16070398

Authors: Abraham Ruiz Gómez Juan Carlos Ferrer Millán José Luis Alonso Serrano Alba Hortal Foronda Susana Fernández de Ávila López

This work presents the fabrication and characterization of biohybrid optoelectronic devices based on the integration of bacteriorhodopsin (bR) and PEDOT:PSS on interdigitated electrodes (IDEs). A three-phase methodology was developed to systematically optimize the active layer. First, the effect of an electric field applied during PEDOT:PSS drying was investigated, identifying a drying voltage of 1.2 V as the optimum among the tested conditions, achieving a maximum responsivity of (35.65±1.36) mA/W, a minimum noise equivalent power of (5.99±0.13)×10−10 W·Hz−1/2, and a maximum specific detectivity of (3.55±0.07)×108 cm Hz1/2W−1. Second, the compatibility of a physiological buffer for bR stabilization was assessed, demonstrating that a 60% PEDOT:PSS/40% buffer composition preserved and further improved the optoelectronic performance of the polymer matrix. Finally, bacteriorhodopsin was incorporated into the optimized PEDOT:PSS/buffer formulation, yielding the hybrid bR_1.2V_60% device, which exhibited an on/off ratio of 2.45 at 0 V and a maximum responsivity of (5.63±0.76) mA/W under reverse bias. Morphological analysis suggested improved film homogeneity, together with a continuous polymer matrix containing dispersed crystalline buffer-salt inclusions. Dynamic measurements showed a stable and reversible short-term photoresponse over successive illumination cycles (ΔION=18.54±0.17μA) with full baseline recovery. These results demonstrate a scalable strategy for the development of biohybrid photodetectors with potential for low-power and sustainable optoelectronic applications.

Biosensors doi: 10.3390/bios16070397

Authors: Bowei Yang Ang Zhou Yuxiang Yang Yu Zhao Chunying Pang

Mid-infrared spectroscopy provides molecular fingerprint information for bacterial biofilm analysis, but the absorption signal of a thin biofilm layer is usually weak. In this work, a metasurface-enhanced mid-infrared biosensor was designed to enhance the fingerprint response of Staphylococcus aureus biofilms. The biofilm transmission spectrum was measured by Fourier-transform infrared spectroscopy, and the film thickness was obtained by atomic force microscopy using an edge step-height method. Based on these measurements, an effective extinction coefficient was extracted and used in finite-difference time-domain simulations. A metal–insulator–metal metasurface was then optimized to cover the main biofilm absorption bands in the mid-infrared region. Two resonator designs were studied: a polarization-dependent structure and a polarization-insensitive structure. The polarization-dependent design showed a strong response under x-polarized incidence and weak coupling under y-polarized incidence. The polarization-insensitive design provided a more balanced response for orthogonal polarizations. At the selected biofilm fingerprint wavelengths, the highest enhancement factors reached 8.57 and 7.24 for the polarization-dependent and polarization-insensitive structures, respectively. Near-field distributions confirmed that the enhancement mainly originated from localized electric fields at the metal resonator edges. These results provide a proof-of-concept design strategy for enhancing weak mid-infrared fingerprint signals from S. aureus biofilms.

Biosensors doi: 10.3390/bios16070396

Authors: Yueqi Yang Jing Li Xinyi Hu Zong Dai Jianhe Guo

Pathogenic bacterial infections remain a persistent global public health crisis. However, traditional clinical detection methods—such as culture-based assays and polymerase chain reaction (PCR)—are often time-consuming and labor-intensive, and they lack sufficient sensitivity for low-abundance pathogens, hindering rapid point-of-care diagnosis. With label-free, ultra-sensitive molecular fingerprinting, Surface-Enhanced Raman Scattering (SERS) has emerged as a powerful tool for rapid pathogen identification. This review summarizes the evolution of SERS-based bacterial detection from fundamental nanomaterials to integrated clinical diagnostic platforms. The article explores four core dimensions: functional integration and enrichment strategies of colloidal probes; structural design and multifaceted capture mechanisms of solid substrates; synergistic advantages of microfluidic systems in enabling automated “sample-to-answer” architectures; and the translational potential of SERS-based lateral flow assays (LFAs) for robust point-of-care testing (POCT). This review reveals a research shift from maximizing electromagnetic enhancement toward overcoming matrix effects in clinical samples and ensuring robustness. In synergy with microfluidics, LFAs, and AI, SERS technology is bridging the bench-to-bedside gap, offering a roadmap for next-generation decentralized, high-precision diagnostics.

Biosensors doi: 10.3390/bios16070395

Authors: Barbara R. Geraldino Nilséia A. Barbosa Priscila M. Galdino Eduardo X. F. G. Migon Danielle Godoy Tatiana Cunha Fernando M. Araújo-Moreira

Nano-carbon biointerfaces offer versatile platforms for cancer biomarker detection, but their progression from analytical proof-of-concept to clinically usable diagnostic evidence remains uneven. This scoping review maps 191 primary studies published from 2024 to 2026, covering nano-carbon families, surface chemistries, transduction architectures, biological matrices, and translational endpoints in cancer biosensing. The evidence space spans four nano-carbon dimensional classes: zero-dimensional carbon dots and quantum dots, one-dimensional carbon nanotubes, two-dimensional graphene-derived materials, and three-dimensional hybrid composites. Across these platforms, analytical sensitivity did not scale monotonically with nano-carbon dimensionality; instead, performance was shaped by the interaction between material architecture, biointerface chemistry, recognition strategy, transduction modality, and matrix context. A Translational Readiness Matrix showed that approximately 67% of studies remained at Low Evidence Level, whereas only approximately 10% reached Strong Evidence Level. Five recurring bottlenecks constrained translation: incomplete reproducibility reporting, limited Real-matrix Validation, scarce comparator-based clinical evidence, insufficient manufacturing-scale data, and weak regulatory or deployment planning. To address these gaps, this review proposes a nine-item minimum reporting checklist and a four-stage validation roadmap to support more reproducible, comparable, and clinically oriented nano-carbon biosensor development.

Biosensors doi: 10.3390/bios16070394

Authors: Sayantan Ghosh Padmanabhan Sindhujaa Pradakshana Senthil Kumar Anand Mohan Pachaiyappan Mahalakshmi Balázs Gulyás Domokos Máthé Parasuraman Padmanabhan

Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge–cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k = 5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.

Biosensors doi: 10.3390/bios16070393

Authors: Rachel L. Warren Alexander R. Pueschel Wei W. Yu Ian M. White

Children who are exposed to lead may have extensive health problems, in particular intelligence deficits and developmental delays. Wide-reaching screening programs are essential to identify children in need of remediation and medical intervention. Lead exposure is most problematic in low- and middle-income countries, as well as in underserved populations in wealthier regions of the world. To increase accessibility, it is critical that screening tools are inexpensive, portable, and easy to use. Here we report a low-cost, handheld, sample-to-answer system for the detection of lead in whole blood samples. Our assay simultaneously lyses blood cells, liberates lead from hemoglobin, aggregates proteins and cellular debris, and separates the solubilized lead from the aggregate via a simple filtration device. Using a screen-printed carbon electrode, anodic stripping voltammetry with a low-cost potentiostat, and our sample-to-answer workflow, we achieved a limit of detection of 1.44 μg/dL, which is below the blood lead reference value established by the US Centers for Disease Control and Prevention (3.5 μg/dL). We validated our system using pre-quantified reference samples from lead-exposed animals and demonstrated excellent agreement with our calibration curve, including for samples near the 3.5 μg/dL threshold.

Biosensors doi: 10.3390/bios16070392

Authors: Haya Akkad Fatih Ciftci Esma Ahlatcıoğlu Özerol Ahmet Akif Kizilkurtlu

Wearable biosensors have swiftly progressed from stiff laboratory prototypes to flexible, skin-like systems capable of ongoing physiological monitoring. Yet, the active and mechanically intense nature of athletic performance reveals the limits of static device designs made solely through traditional 3D printing. This review offers a thorough analysis of the shift from custom 3D-printed platforms to adaptive 4D-printed wearable biosensors that include time-sensitive, stimuli-responsive materials. We carefully investigate how nanomaterial-engineered transducers, including carbon nanomaterials, MXenes, and metallic nanostructures, improve electrochemical sensitivity, signal stability, and mechanical durability in sweat-based and electrophysiological sensing. Additionally, we examine the integration of thermoresponsive polymers, moisture-activated hydrogels, shape-memory materials, and self-healing networks that support autonomous control of skin–sensor contact, microfluidic sweat management, and structural stability under high mechanical strain. Case studies focused on sports monitoring demonstrate how these innovations enable multimodal measurement of mechanical, chemical, and molecular biomarkers in real time. Lastly, we address manufacturing scalability, regulatory issues, and translational challenges necessary to move from proof-of-concept to clinical deployment. By combining nanomaterial-driven electrochemical precision with 4D-printed mechanical adaptability, this review maps a path toward adaptive, self-regulating wearable platforms that sustain analytical accuracy even under extreme physiological demands.

Biosensors doi: 10.3390/bios16070391

Authors: Hao Li Xinyu Zhang Qikai Wang Jun Ma

Parkinson’s disease (PD) is associated with gait impairment, bilateral asymmetry, and increased gait variability, highlighting the need for objective and interpretable wearable gait assessment. Plantar insole recordings directly capture foot–ground loading, but their use in PD assessment is often limited by global or low-order descriptors that do not fully represent regional loading organization. This study proposes a region-specific information-theoretic framework for PD gait assessment using wearable plantar-pressure insoles. Bilateral plantar insole signals were reorganized into five anatomical regions: heel, rearfoot, midfoot, forefoot, and toe. Self-information index (SII), Shannon entropy (EN), negentropy (NEG), sample entropy (SEN), and Kullback–Leibler divergence (KL) features were extracted to characterize self-information fluctuation, probabilistic uncertainty, non-Gaussian organization, temporal irregularity, and directional distributional discrepancy in plantar-pressure dynamics. The resulting feature representation was evaluated at gait-cycle, walking-recording, and subject-independent levels using conventional classifiers, ablation analysis, subject-balanced cycle aggregation, and an information-theoretic three-dimensional feature-space rule model (ITFS-RM). KNN achieved an accuracy of 0.9668 at the gait-cycle level, and MLP achieved an accuracy of 0.9344 at the walking-recording level. Under stricter subject-independent evaluation, the accuracy was 0.8475, and subject-balanced-cycle aggregation achieved an accuracy of 0.8655. Region-specific analysis and ablation experiments showed spatially heterogeneous HC–PD differences, with the toe region showing the most consistent contribution. SII, KL, and NEG provided stable discriminative contributions, particularly in toe-related and regional-transition features. ITFS-RM provided explicit feature combinations, value ranges, and spatial rule boundaries for interpretable walking-recording level and subject-grouped separation. These results support region-specific information-theoretic analysis as an interpretable representation of plantar-pressure dynamics for PD gait assessment and emphasize the need for subject-wise validation when repeated walking recordings are available.

Biosensors doi: 10.3390/bios16070390

Authors: Stuart Knowling Kirsty McBain David Apiyo

Biolayer Interferometry (BLI) has traditionally been used for characterization of protein–protein interactions (PPI) with proteins, such as antibodies and their antigens, through kinetic and quantitation assays. Limitations, for example in sensitivity and the availability of established assay formats, have restricted its adoption across other analytical applications. This article highlights three case studies which demonstrate the expansion of BLI into novel applications, spanning the areas of protein detection and viral vector characterization. The first case study details the use of a multi-step signal amplification assay to enable the detection of low abundant molecules, such as cytokines, at lower concentrations than can be detected using the standard one-step binding approach. Cytokines are sandwiched between biotinylated and HRP-conjugated antibodies, then dipped into 3-amino-9-ethylcarbazole (AEC) reagent, resulting in an enhancement of the cytokine detection sensitivity. The second case study uses BLI for the quantitation of mixed populations of a bispecific antibody (bsAb). Bridging and dual binding assay formats are evaluated for their assessment of bsAb antigen binding kinetics and ability to determine the ratio of correctly assembled bsAb within a sample. In the third case study, BLI detection principles are used to estimate the percentage full capsids in a mixed population of AAV particles. Collectively, these case studies demonstrate the versatility of Octet® BLI and highlight its potential to support an increasing range of analytical workflows beyond its traditional applications.

Biosensors doi: 10.3390/bios16070389

Authors: Hong Liang Jiahao Tan Tingyu Wang Yaomei Wang Chen Zhang

Sulfadimethoxine (SDM) is a sulfonamide antibiotic widely used in the aquaculture of aquatic organisms. Its excessive residues in animal-derived food products can cause irreversible harm to human health and the environment. Current primary detection methods for SDM, such as instrumental methods and Immunoassay techniques, demonstrate high sensitivity and accuracy. However, their industrial application is impeded by laborious sample pretreatment, reliance on specific equipment, and dependence on specially trained personnel. Therefore, there is an urgent need to develop a simple and rapid method for detecting SDM residues. In this study, we constructed a novel colorimetric sensing platform based on functional nucleic acids for SDM detection. This sensor incorporates a nucleic acid aptamer capable of specifically recognizing SDM, a G-quadruplex/Hemin complex with peroxidase-like catalytic activity, and a shielding sequence that suppresses catalytic activity while undergoing SDM-induced conformational changes. The colorimetric signal was generated using a 3,3′,5,5′-Tetramethylbenzidine (TMB) chromogenic substrate, and the sensor’s performance was evaluated via absorbance measurements with a microplate reader. After optimizing detection conditions, the sensor exhibited a linear response to SDM concentrations ranging from 0.155 to 3.10 ng/mL, with a detection limit of 0.0796 ng/mL. Furthermore, the sensor demonstrated excellent selectivity and achieved recoveries of 83.0% to 107% in spiked aquaculture water and fish samples, with coefficients of variation below 10.4%, confirming its superior practicality for real-world sample analysis.

Biosensors doi: 10.3390/bios16070388

Authors: Yaning Liang Shiyi Fang Zhuoer Chen Yuzhuo Chen Qingqing Yang Xuelan Shu Tao Le

Gold nanoparticles (AuNPs) are widely used in aptasensors because of their high extinction coefficient and aggregation-dependent color differences. However, recent studies have indicated that nonspecific interactions between target molecules and AuNPs may dominate the detection signal rather than aptamer–target specific binding. This study systematically investigated the interactions between 13 aminoglycoside antibiotics and AuNPs. We found that all aminoglycoside antibiotics interacted strongly with AuNPs, considerably reducing their salt stability. Furthermore, methoxy polyethylene glycol thiol reversed AuNP aggregation induced by aminoglycoside antibiotics, indicating that it occurs at the secondary minimum. Using density functional theory, we analyzed the molecular structures and charge distribution characteristics of the aminoglycoside antibiotics, elucidating that they replace citrate ions on AuNP surfaces via a ligand exchange mechanism, thereby inducing aggregation. Additionally, both aptamer targets and complementary DNA struggled to desorb the aptamer (KAN6-1) from the AuNP surfaces. Our study demonstrates that the label-free colorimetric assay based on aggregation of unmodified citrate–AuNPs is neither suitable for characterizing the binding affinity of aminoglycoside aptamers nor viable for constructing corresponding colorimetric sensors to detect this class of antibiotics. Thus, researchers should incorporate mechanistic verification and rigorous controls when employing this system to ensure reliable results.

Biosensors doi: 10.3390/bios16070387

Authors: Juan Pablo Hervás-Pérez Sergio Izcara Marta Sánchez-Paniagua

The reliable detection of the peanut allergen Ara h2 in processed foods remains a major challenge, since thermal and high-pressure treatments can alter protein structure and limit the performance of immunoassays. DNA-based methods provide a robust alternative to this approach. In this work, a highly sensitive label-free electrochemical genoassay for Ara h2 DNA detection was developed using streptavidin-coated magnetic beads (MBs). A biotinylated capture probe (CP) immobilized on the MBs’ surface enabled specific target recognition through a sandwich hybridization strategy with a secondary probe, allowing for direct electrochemical detection without enzymatic labels. Two transduction strategies were evaluated: (i) electrochemical impedance spectroscopy (EIS) with ferri/ferrocyanide as a redox probe, and (ii) differential pulse voltammetry (DPV) using methylene blue. The ferri/ferrocyanide-based EIS approach showed the best sensitivity and discrimination between hybridized and non-hybridized states. A linear dependence was observed with the concentration of the synthetic Ara h2 target over the 0.05 to 20 nM range, with a detection limit of 0.025 nM. CP-MBs showed good stability for at least 20 days. Applicability was demonstrated in soy beverages, rice beverages, and low-fat cow’s milk, with recoveries close to 100% and negligible matrix effects.

Biosensors doi: 10.3390/bios16070386

Authors: Shuyi He Weichao Liu Kang Qin Steven Xu Wu

Graphene quantum dots (GQDs) have attracted extensive interest in biomedical applications because of their favorable physicochemical properties, including environmental friendliness, excellent water solubility, high chemical stability, and facile surface modification. However, most GQDs exhibit fluorescence in the ultraviolet or visible region, which limits their biomedical applications because autofluorescence from biological systems reduces the signal-to-noise ratio in biosensing and bioimaging. Over the past decade, the emission of GQDs has been extended from the UV–visible region into the red-to-near-infrared (NIR) region. Red-to-NIR fluorescence enables higher-resolution imaging and deeper tissue penetration by reducing light scattering and minimizing tissue absorption and autofluorescence. In this review, we summarize recent advances in red-to-NIR-fluorescent GQDs for biomedical applications, including their synthesis, optical properties, surface engineering, and applications in biosensing, bioimaging and theranostics. Finally, we discuss the current challenges and future potential development of the red-to-NIR-fluorescent GQDs.

Biosensors doi: 10.3390/bios16070385

Authors: Arafat Meah Shuo Yin Saimoen Strrrz Anderson Ming Lin Shuo Liang Meghana Davuluri Yi-Xian Qin Sandeep K. Mallipattu Jun Wang

Direct antibody labeling is essential for immunoassays, multiplexed imaging, and biosensing; however, current methods are often time-consuming, restricted by antibody source, risk compromising protein performance, or vary with multiple steps. We introduce multiplex in situ tagging (MIST) Linker, a rapid and Fc-site-specific labeling tool that conjugates fluorophores or DNA oligonucleotides to antibodies from diverse commercial sources in as fast as 10 min using minimal starting material. MIST Linker achieves >90% cleavage upon UV exposure, facilitating rapid cyclic imaging on a single specimen. Validated across multiple species and sources, the platform outperforms conventional two-step immunofluorescence and immunohistochemistry in various tissues and cell lines. By enabling the rapid, cost-effective customization of antibody panels, MIST Linker significantly lowers the barrier to accessing antibody–DNA conjugates for spatial biology. When integrated with the spatial MIST platform and MIST-Explorer, it enables high-plex, single-cell spatial proteomics at high signal-to-noise ratios in human clinical biopsies, mouse specimens and cell lines. This toolkit provides an efficient, accessible solution for high-resolution spatial mapping, allowing for the in-depth analysis of cell subpopulations, biomarker distributions, and signaling events in complex biological specimens. Thus, MIST Linker offers a versatile, accessible, and scalable solution for antibody-labeling-based research and clinical diagnosis.

Biosensors doi: 10.3390/bios16070384

Authors: Sumedha Nitin Prabhu

Exosomes and small extracellular vesicles are promising liquid-biopsy biomarkers because they carry molecular information from their cells of origin and can be accessed from minimally invasive biofluids. Reliable separation and detection are made more difficult by their small size, low abundance, diverse composition, and co-occurrence with lipoproteins, protein aggregates, and other extracellular particles. To improve exosome enrichment, capture, and sensing, nanomaterial-assisted techniques have become crucial. Using a mechanism-based approach that differentiates between non-gravimetric signal amplification and genuine physical mass loading, this study offers an organized comparison of nanomaterial-enabled exosome sensing techniques. This distinction is helpful because different transducers measure different physical quantities: while optical, electrochemical, fluorescent, catalytic, and nucleic acid-based platforms typically benefit from enhanced signal generation rather than increased mass, resonant and gravimetric sensors benefit from increased inertial or surface-bound mass. In terms of amplification mechanism, transducer compatibility, sample-matrix tolerance, workflow complexity, and translational maturity, the review contrasts metallic nanoparticles, magnetic systems, metal–organic frameworks, carbon and two-dimensional materials, quantum dots, upconversion nanomaterials, DNA nanostructures, and polymer-based platforms. The gap between analytical sensitivity and clinical utility, including separation purity, recovery, biological heterogeneity, pre-analytical variability, interference from complex biofluids, and the need for uniform validation, is given special focus. The review concludes that no single nanomaterial or amplification method is universally optimal; instead, platform-aware, application-specific integration of isolation, amplification, and validation techniques is necessary for clinically meaningful exosome sensing.

Biosensors doi: 10.3390/bios16070383

Authors: Rami Alazrai Oula Hatahet Sahar Qaadan Youssef Alothman Mohamed Bader-El-Den

Brain–computer interface (BCI) systems have demonstrated significant potential across medical, educational, and entertainment domains. Recently, visual imagery (VI) has emerged as an alternative to traditional motor imagery (MI) paradigms, offering a broader spectrum of control signals for dexterous assistive devices. In this study, we propose a novel BCI framework for classifying visually imagined arrows defined by different colors and directions. The proposed framework employs the Choi–Williams time–frequency distribution (CW-TFD) to construct a joint time–frequency–spatial representation (TFSR) of EEG signals. The resulting TFSR is converted into grayscale images and provided as input to a newly designed convolutional neural network (CNN), which performs 16-class decoding of visually imagined arrows defined by combined color and direction attributes. A new EEG dataset was collected from 16 subjects who imagined 16 distinct arrows comprising four colors and four directions. The framework achieved an average classification accuracy of 95.05% and a Cohen’s kappa score of 0.947 across the 16 classes. To comprehensively evaluate the proposed approach, three comparative analyses were conducted. First, multiple time–frequency representations were assessed for VI-based EEG decoding. Second, the proposed CNN architecture was benchmarked against several state-of-the-art pre-trained deep learning models. Third, the framework was compared with conventional machine learning classifiers using handcrafted features. Results demonstrate that the constructed CWD-based TFSR combined with the proposed CNN consistently outperforms alternative representations and classification models. These findings demonstrate the feasibility of decoding an expanded set of visually imagined color–direction arrow commands in a subject-specific EEG-based BCI setting, supporting further development of calibrated VI-based BCI systems for assistive and interactive applications.

Biosensors doi: 10.3390/bios16070382

Authors: Yunseon Han Haebin Jo Minyoung Ju Seowoo Bae Ju Young Kim Jinho Yoon Taek Lee

The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, accurate, and point-of-care diagnostic technologies, accelerating interest in biosensors as next-generation analytical platforms. However, biosensor performance is governed by a connected sequence of processes, including bioprobe–target recognition, sensor fabrication, structural optimization, and signal interpretation. Because these processes involve multiple interacting variables, conventional empirical approaches often have limitations in efficiently optimizing biosensor performance and interpreting complex analytical signals. Artificial intelligence (AI) and machine learning (ML) provide tools to model these relationships and support prediction-guided biosensor development. This review discusses recent progress in AI-assisted biosensor development in three sequential stages. First, AI-assisted bioprobe design is reviewed, including in silico aptamer discovery, smart-SELEX-based aptamer screening, and peptide receptor design for improving molecular recognition. Second, AI-driven sensor fabrication and structural optimization are discussed, focusing on electrochemical feature extraction, paper-based microfluidic device optimization, and optical biosensor parameter prediction. Third, ML-based signal analysis is examined as a strategy for converting complex electrochemical, colorimetric, and optical responses into quantitative analytical outputs. By organizing these examples as a connected workflow rather than as separate applications, this review highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors.

Biosensors doi: 10.3390/bios16070381

Authors: Bo Jiang Han Liu Yuchen Ran Yan Zhou Keke Chen Xiao Yang Jiayuan Zhao Mengxuan Hu Boyan Fang Guangying Pei

Parkinson’s disease (PD) is a neurodegenerative disorder affecting motor and autonomic nervous system functions. In this study, six synchronized modalities—electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), respiration (Resp), photoplethysmography (PPG), and gait (Gait)—were recorded from 25 PD patients and 25 healthy controls. A Random Forest classifier was used to perform both unimodal and multimodal signal classification. Among unimodal models, ECG achieved the highest accuracy (84%), whereas the performance of multimodal combinations did not increase linearly with the number of modalities; integrating three or more complementary signals was sufficient to substantially improve classification. The full six-modality model achieved an accuracy of 95.00%, precision of 94.17%, recall of 97.14%, F1 score of 95.21%, and an AUC of 0.98. Incremental analysis further indicated that selecting key complementary modalities can maintain high classification performance while reducing equipment requirements, simplifying experimental procedures, and improving participant comfort, providing guidance for the development of efficient, non-invasive PD diagnostic tools.

Biosensors doi: 10.3390/bios16070380

Authors: Shota Tsuchiya U. Yanagi Hoon Kim Kei Shimonosono Naoki Kagi

Fluorescent aerosol particles (FAPs) are widely used as a real-time proxy for primary biological aerosol particles; however, their seasonal characteristics and size-resolved distributions in office environments remain poorly understood. In this study, FAPs were measured in ten office spaces located in four distinct regions of Japan during summer and winter using a real-time Bioaerosol Sensor. Indoor and outdoor FAP concentrations, indoor/outdoor ratios, and the size-resolved FAP fraction were evaluated. Indoor FAP concentrations were generally below 100 particles per liter (p/L), although peak concentrations of 140 p/L in summer and 195 p/L in winter were observed. Significant seasonal differences were detected in most offices, with several buildings showing higher concentrations in winter. Many offices exhibited relative humidity levels below 40% during winter, suggesting that dry indoor conditions may have promoted particle resuspension and contributed to elevated FAP concentrations. Indoor–outdoor comparisons suggested contributions from both indoor sources and outdoor infiltration. The size-resolved FAP fraction increased markedly with particle size, with median indoor values reaching 40–74% for 2.0–5.0 μm particles and 96–100% for particles > 5.0 μm. These findings indicate that FAPs in office environments are strongly associated with coarse particles and exhibit substantial seasonal and building-dependent variability.

Biosensors doi: 10.3390/bios16070378

Authors: Jingfang Zhang Wenhui Mao Shiqi Xia Liangshu Hu Mingzhang Guo Huilin Liu

Polycyclic aromatic hydrocarbons (PAHs) and their halogenated derivatives are important targets in environmental monitoring and pollution control because of their persistence, bioaccumulation, and potential carcinogenicity. Reliable strategies for detecting these pollutants remain essential for environmental risk assessment. In recent years, microbial whole-cell biosensors have attracted increasing attention as analytical tools for pollutant detection and toxicity evaluation. These biosensors employ living cells to recognize target compounds and generate measurable signals through endogenous metabolic pathways and transcriptional regulatory networks. As a result, they can reflect biologically relevant responses and operate in complex environmental matrices, making them suitable for in situ monitoring. This review summarises recent advances in whole-cell biosensors for detecting PAHs and their halogenated derivatives. We discuss the design strategies for constructing these whole-cell biosensors and outline their technological development. Recent efforts to improve biosensor performance are also highlighted. Current research trends indicate a shift from optimizing individual genetic components to improving overall system robustness, standardized evaluation, and practical field deployment. These developments provide important insights for designing reliable and engineerable whole-cell biosensing platforms for monitoring PAHs and related pollutants.

Biosensors doi: 10.3390/bios16070379

Authors: Xinyu Yang Yuting Sun Hong Jin Jianguo Feng Shangzhong Jin

Given that red blood cells (RBCs) are the most abundant cells in blood, their morphology and mechanics strongly affect blood rheology. Furthermore, changes in the physiological functions and health status of an organism can also affect RBC mechanics. Therefore, understanding the mechanical properties of RBCs holds substantial research value in the biomedical field. The technology of optical tweezers (OT) has become a crucial method for measuring and analyzing the mechanical properties of RBCs, owing to their unique advantages such as non-contact manipulation and piconewton-level force sensitivity. This review first outlines the basic mechanical properties of RBCs, the mechanical sensing principles of optical tweezers, and their basic manipulation modes. It also focuses on the measurement and application of key mechanical parameters, such as the deformation index and shear modulus. Furthermore, the review covers the integration of optical tweezers with Raman spectroscopy, fluorescence, and microfluidics. These combined approaches allow for the simultaneous acquisition of mechanical and molecular data, dynamic monitoring of mechanical state changes, and analysis of external stimuli and physiological mechanisms, thereby supporting disease diagnosis, drug efficacy evaluation, and artificial blood quality assessment. Finally, it discusses current challenges and future directions.

Biosensors doi: 10.3390/bios16070377

Authors: Qiangqiang Ouyang Ziming Gao Jingbo Yang Shaoyi Wang Zonglin Li Yifan Zhang Tianyou Chen Xinhua Xu Runkun Han Hao Chen

Lymph node metastasis (LNM) is a key prognostic factor in esophageal squamous cell carcinoma (ESCC), and accurate preoperative prediction remains challenging. Blood biomarkers provide a conventional, preoperative diagnostic technique that is cost-effective and free from radiation risks. So far, previous studies have been published on the precise diagnosis of lymph node metastases using conventional ultrasound or CT techniques. While there is a lack of research studies that address the diagnosis of LNM from blood biomarkers. In this work, we acquired a cohort of blood biomarkers of 1933 patients and designed a weighted neural network (WNN) model for the accurate prediction of LNM from blood biomarkers. The WNN model is designed with a neural network classifier trained on blood biomarkers labeled with pathological nodal (pN) stages of LNM. The experimental findings demonstrate that the WNN model achieved 83.1% accuracy and an AUC of 0.88 on the original, non-augmented test set for diagnosing LNM, while CT only achieved 50.4% accuracy (AUC 0.60) and ultrasound achieved 60.5% accuracy (AUC 0.67). Additionally, SHAP analysis reveals that three blood biomarkers—white blood cells (WBC#), monocytes (Mono#), and neutrophils (Neut#)—significantly impact the WNN model’s output. This WNN model shows promise as a research tool to diagnose LNM.

Biosensors doi: 10.3390/bios16070376

Authors: Mengli Wang Yuanyuan Xia Yulei Li Lifen Chen Kunyan Wang Shuangshuang Wu Yuelan Zhang

In this work, we developed a novel, ultrasensitive fluorescence sensing platform for determination of organophosphorus pesticides (OPs), using chlorpyrifos as a representative model analyte. The sensing strategy was constructed upon the key inner filter effect (IFE) between graphitic carbon nitride (g-C3N4) nanosheets and silver-coated gold core–shell nanoparticles (Au@Ag NPs). Initially, gold nanoparticles (Au NPs), silver nanoparticles (Ag NPs), and Au@Ag NPs were successfully synthesized, and their fluorescence quenching efficiencies toward g-C3N4 were systematically evaluated. Owing to the superior spectral overlap with the fluorescence emission of g-C3N4, Au@Ag NPs exhibited the most obvious quenching effect and were thereby selected as the optimal quencher for sensor fabrication. Then, acetylcholinesterase (AChE) catalyzed the hydrolysis of acetylthiocholine (ATCH) into thiocholine. The generated thiocholine then induced aggregation of Au@Ag NPs via electrostatic and Ag-S interactions, which reduced the IFE efficiency and ultimately restored the fluorescence of g-C3N4. In contrast, the presence of chlorpyrifos effectively inhibits AChE activity, thereby suppressing ATCH hydrolysis and the subsequent aggregation of Au@Ag NPs. The fluorescence intensity of g-C3N4 was quenched by Au@Ag NPs and the signal was low. Under optimal experimental conditions, the response signal was found to be proportional to chlorpyrifos (CPF). This work presents a rapid, cost-effective, and highly sensitive approach for CPF residue analysis, holding great potential for applications in food safety monitoring and environmental surveillance.

Biosensors doi: 10.3390/bios16070375

Authors: Zhaochi Chen Minh-Quang Tran

A capillary-driven microfluidic electrical screening platform was developed using silver nanoparticle (AgNP)-decorated laser-patterned villous microstructures on a glass substrate for the analysis of H3N2-infected A549 cells. The device integrated nanosecond laser patterning, AgNP conductive thin-film formation, passive capillary transport, and direct electrical readout within a single microfluidic sensing structure. Villous-like arrays were fabricated using a 1064 nm IR pulsed laser at a fluence of 4.35 J/cm2, with a repetition rate of 300 kHz, pulse overlap of 96.7% and scanning speed of 500 mm/s. The fabricated structures exhibited a diameter of 60 μm, height of 80 μm and interpillar pitches ranging from 30 to 90 μm. After AgNP deposition, the surface showed a dominant Ag content of 59.2%, confirming successful formation of conductive microstructured electrodes. The 30 μm pitch structure produced the highest current response of 22 μA at 1 V and the highest ΔInorm of 0.053 after introduction of H3N2-infected A549 samples. Wettability and capillary transport were tunable by pitch, with contact angles (CAs) decreasing from 140° to 30° and flow velocities decreasing from 0.1 mm/s to 0.03 mm/s. Formalin-fixed H3N2-infected A549 cells were electrically distinguished from non-infected A549 controls over 101–106 PFU/μL, with detectable responses down to 101 PFU/μL. These results demonstrate a label-free, self-driven, and fabrication-oriented microfluidic strategy for electrical screening of virus-associated cellular samples.

Biosensors doi: 10.3390/bios16070374

Authors: Helena Kosnacova Dusan Horvath Diana Vitazkova Erik Foltan Michal Pecik Erik Vavrinsky

Wearable electroencephalography (EEG) is rapidly evolving toward lightweight, user-friendly systems that enable brain monitoring in naturalistic settings. Traditional multi-channel, gel-based systems provide broad scalp coverage and high signal fidelity but are impractical for unsupervised or long-term use. This review focuses on the emerging generation of smart wearable EEG devices that are easy to wear, require minimal setup, and typically integrate additional physiological sensors such as photoplethysmography (PPG), temperature, or motion sensors. We review wearable EEG systems across four main form factors: head-worn EEG devices, smart EEG patches and tattoos, in-ear and headphone-based EEG, and glasses-integrated EEG. Head-worn systems offer broader signal coverage and support more complex applications such as sleep staging, human–machine interaction, and epilepsy monitoring. Patch-based systems are well suited to comfortable long-term monitoring, particularly in sleep-related applications. Ear-center systems provide high user comfort and stable signal acquisition from non-traditional electrode locations. Glasses-integrated devices represent an emerging option for unobtrusive daytime neurophysiology. Each category is examined in terms of sensor fusion, technical parameters, and embedded algorithms, with particular emphasis on automated signal analysis. We conclude with a discussion on current limitations, regulatory and usability challenges, and future directions toward unobtrusive, AI-powered neurotechnology for home and clinical use.

Biosensors doi: 10.3390/bios16070373

Authors: Manjyot Kaur Janesh Kumar Gautam Aishwarya Rajendra Sharma Vishal Singh Disha Chouhan Akash Baghel Bontha V. Babu Suman Sundar Mohanty

Sickle cell disease (SCD) and thalassemia are genetic disorders that necessitate accurate diagnosis for effective management and improved patient outcomes. The advent of nanotechnology has paved the way for innovative, precise detection methods, offering enhanced sensitivity and specificity. The present systematic review aims to assess the analytical performance of nanotechnology-based detection methods for SCD and thalassemia, with a focus on evaluating the analytical performance and identifying the most sensitive nanotechnology-based techniques. An extensive literature search was conducted across five databases (ScienceDirect, PubMed, Embase, Google Scholar), yielding 23 studies that met the inclusion criteria. These studies showcased the potential of nanotechnology-based methods for detecting SCD and thalassemia. The studies utilized diverse samples, including blood, serum, genomic DNA, and purchased oligonucleotides, with most reporting limit of detection (LOD) values. Specifically, gold nanoparticles (AuNPs) exhibited exceptional sensitivity, with detection limits ranging from 2.6 aM to 0.035 pM. Surface modification and functionalization of AuNPs significantly enhance their detection capabilities. Other nanostructures, including silver nanoparticles, quantum dots, and graphene quantum dots, also demonstrate promising diagnostic capabilities. The results showed that nanotechnology-based methods demonstrated improved analytical sensitivity, with LOD ranging from 2.6 aM to 50 nM. This systematic review provides a comprehensive overview of the analytical performance of nanotechnology-based detection methods, shedding light on their potential to revolutionize diagnosis and treatment. Overall, it highlights the transformative potential of nanotechnology in improving molecular diagnostic accuracy for SCD and thalassemia.