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.

Biosensors doi: 10.3390/bios16070372

Authors: Ling Yang Dandan Peng Haihu Tan Yahu Wang Xin Lu Fanming Zeng Shigang Liu Yuejun Liu

Accurate discrimination of multiple heavy metal ions is essential for environmental monitoring. This study developed a simple fluorescent sensing array utilizing carbon dots derived from black plum peels (PCDs) for the precise identification of metal ions in environmental waters. Three structurally distinct PCDs were hydrothermally synthesized using phenylenediamine isomers as nitrogen dopants, exhibiting distinct fluorescence response patterns to target ions. Pattern recognition was performed using linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA). The optimized system (pH 5–7) achieved high discrimination accuracy for eight metal ions (Sn2+, Ag+, Hg2+, Fe3+, Cr3+, Pb2+, Sb3+, and Cu2+) at 5–400 μM concentrations. The array effectively identified the binary and ternary mixtures of Hg2+/Cu2+/Cr3+ and successfully detected target ions in river water samples. This cost-effective and scalable approach demonstrates strong potential for applications in water quality monitoring and food safety.

Biosensors doi: 10.3390/bios16070371

Authors: Francesco Arcadio Alessandro Capo Alessia Calabrese Chiara Marzano Mimimorena Seggio Rosalba Pitruzzella Federica Passeggio Shahab Bashir Muhammad Shoaib Carla Zannella Anna De Filippis Giuseppe Portella Luigi Zeni Nunzio Cennamo

The Enterobacteriaceae family is a significant source of foodborne pathogens and represents a severe threat to human and animal health. These bacteria can penetrate the dairy supply chain through direct contact with cattle and the livestock environment and can survive production processes. Escherichia coli (E. coli), one of the most diffuse bacteria in raw and processed milk, exposes consumers to the risk of contaminated milk. As a result of this exposition, several milk-borne illness outbreaks have been reported worldwide, underscoring the urgent need for effective detection and prevention measures. Conventional analysis methods are effective but have significant limitations, including the requirement of pre-treatment and pre-enrichment steps. Thus, the need for advanced detection techniques that can accurately identify these pathogens without pre-treatment steps is critical. In this work, a proof-of-concept biosensor based on a spoon-shaped optical biochip was developed to detect E. coli via surface plasmon resonance (SPR) phenomena and was combined with a polyclonal antibody layer against E. coli as a molecular recognition element (MRE). The proposed label-free biosensing strategy, achieved by exploiting simple SPR spoon-shaped biochips, exhibits a remarkable detection limit (6.8 colony-forming units, CFU/mL) and high specificity towards other interfering bacteria belonging to the Enterobacteriaceae family. In addition, tests on commercial milk samples were carried out, achieving recovery values of 95% and 102% for whole milk and infant milk, respectively. The proposed spoon-shaped biosensor enables label-free biosensing without the need for microfluidic systems. It provides a rapid response (10 min), paving the way for its use as a point-of-care test (POCT) in real-world settings.

Biosensors doi: 10.3390/bios16070370

Authors: Liu Yang Changhua You Gang Wang Xuan Zhang Canyang Wang Bo Cheng Zhengtuo Zhao Ning Xue Lei Yao

Distributed neural interfaces for multi-region implantation require both scalable interconnects and robust telemetry, yet conventional centralized or fully distributed architectures often trade-off wiring complexity, resource reuse, and transmission stability. This work presents a distributed wireless neural recording system based on a parallel-link architecture and a custom 12-channel neural recording Application-Specific Integrated Circuit (ASIC). Each remote module is connected to a central hub through an independent four-wire link (VDD/GND/LVDS±). The ASIC integrates modular digital pixels (MDPs), an on-chip oscillator, a Manchester encoding, and a Low-Voltage Differential Signaling (LVDS) output to reduce interconnect count while maintaining reliable serial transmission. Fabricated in SMIC 0.18 μm CMOS, the chip occupies 4.84 mm × 0.36 mm and consumes 10.13 mW in total, with 48.5 μW/channel consumed by the recording channels excluding the LVDS driver. It achieves 5.6 μVrms input-referred noise and a measured per-channel sampling rate of 28.93 kSps. A compact 20 mm2 recording module and an FPGA-based central hub with real-time decoding and compression were implemented for validation. In vivo mouse experiments demonstrate clear action-potential recordings across 12 channels, confirming the feasibility of stable and scalable multi-region neural signal acquisition.

Biosensors doi: 10.3390/bios16070369

Authors: Alessio Cabizosu Victor Ruiz-Angui Carmen Carazo-Díaz Francisco Javier Martínez-Noguera Pedro E. Alcaraz

Introduction: Cross-country running performance is influenced by a complex interaction of physiological, biomechanical, and morphological factors. Recently, infrared thermography (IRT) has emerged as a non-invasive method to assess skin temperature (TSK) and detect potential asymmetries associated with neuromuscular status, fatigue, and injury risk. However, limited evidence exists regarding its relationship with competitive performance in endurance athletes. Methods: An observational study, conducted with STROBE guidelines, included 24 national-level cross-country athletes competing in the 2026 Spanish National Championships. Pre-competition assessments comprised bilateral thermographic analysis of the anterior and posterior thigh and leg regions, alongside some anthropometric measurements (thigh and leg circumferences) following ISAK standards. Performance was evaluated using official race times. Independent t-tests and linear regression models were applied to assess sex differences and associations between variables. Results: No significant sex differences were observed in thigh circumference, whereas males presented significantly greater leg volume (right p = 0.020; left p = 0.042). Thermographic analysis showed no differences in bilateral thermal asymmetry (ΔTSK) between sex quadriceps (p = 0.077), hamstrings (p = 0.695), shins (p = 0.510), and calves (p = 0.194); however, higher absolute temperatures were observed in males in specific thigh regions (right anterior p = 0.039, right posterior p = 0.015, left posterior p = 0.020). Males achieved significantly faster race times during the first four laps, t1 (p ≤ 0.001), t2 (p = 0.002), t3 (p = 0.002), and t4 (p = 0.008), but there was no difference in the fifth lap, t5 (p = 0.179). Statistically significant correlations were observed between temperature differences in the various anatomical regions and competition results during the first four laps, in three of the four regions analyzed (anterior thigh p = 0.035, posterior thigh p = 0.010, anterior leg p ≤ 0.001). Conclusions: Pre-competition thermal asymmetry of the lower limbs appears to be negatively associated with endurance performance, potentially reflecting suboptimal neuromuscular status or incomplete recovery. IRT represents a practical and sensitive tool for monitoring athletes’ physiological readiness.

Biosensors doi: 10.3390/bios16070368

Authors: Mengli Lv Xiaojie Sun Xinpeng Wang

Global population aging has emerged as a major driver of the growing burden of neurological diseases, highlighting the urgent demand for advances in early diagnosis, prevention, and rehabilitation. These conditions are typically characterized by insidious onset and irreversible progression, yet their clinical management remains critically compromised by substantial diagnostic delays, representing an intractable bottleneck for existing detection technologies. Therefore, the development of precise, early-stage detection technologies is crucial for expanding the therapeutic window and improving long-term clinical outcomes, addressing a critical unmet clinical need. Herein, we review and compare precision detection strategies for neurological diseases, focusing on the types and mechanisms of mainstream biosensing platforms. Based on the classification of detection substrates and signal transduction mechanisms, four major bio-detection branches are analyzed, including liquid, exosomal, imaging, and digital biomarker detection, with representative studies demonstrating detection limits reaching femtomolar concentrations, clinical diagnostic sensitivities exceeding 90%, and classification accuracies comparable to or surpassing conventional imaging modalities. The inherent advantages and limitations of each biosensing technology are also comprehensively discussed. This review underscores that future research on neurological biomarker sensing is trending toward multimodal integration, which enables the construction of more robust early warning and prognostic assessment systems. This work aims to provide valuable theoretical insights for clinical translation of relevant sensing technologies and integrated diagnostic and treatment strategies, thereby facilitating the progress of early intervention and rehabilitation for common neurological diseases.

Biosensors doi: 10.3390/bios16070367

Authors: Jianwei Huang Fan Jia Shaoxiang Duan Bo Liu

Optical fiber multi-analyte biosensors have become an important cutting-edge technology for the simultaneous detection of multiple biochemical substances in complex samples due to their unique advantages such as small size, anti-interference capability, and remote and label-free detection. This paper systematically reviews the recent research progress of optical fiber multi-analyte biosensors in the field of simultaneous detection of various types of targets. The review is organized by detection target type and elaborates on the simultaneous detection of biomarkers and proteins, viruses and bacteria, biological metabolites and nutrients, heavy metal ions, gases, organic pollutants, cells, and mixed detection of different types of biochemical substances. The advantages and disadvantages of existing optical fiber multi-analyte biosensors are summarized. Key technical challenges are also discussed, including issues of selectivity, long-term stability, real-sample validation, and system integration that currently hinder practical deployment. Finally, the future challenges and development directions of optical fiber multi-analyte biosensors are briefly discussed, providing references for relevant research teams.

Biosensors doi: 10.3390/bios16070366

Authors: Liangshu Hu Yipei Yang Shiqi Xia Wenhui Mao Ying Shang Yuzhen Wang Huijuan Yang Mingzhang Guo

Synthetic microbial community (SynCom) biosensors are emerging from the convergence of whole-cell biosensing, synthetic ecology, and computational design. Conventional whole-cell biosensors (WCBs) use a single microbial chassis to convert analyte recognition into optical, electrochemical, gaseous, or growth-linked outputs. This compact architecture supports low-cost and field-oriented detection, but it can be limited by cellular burden, narrow dynamic range, environmental interference, and difficulty in interpreting multicomponent signals. Natural microbial consortia provide an ecological template in which sensing, transformation, stress tolerance, and response are distributed across interacting populations. SynCom biosensors seek to translate this logic into engineered platforms with defined members, assigned functional roles, designed communication, and interpretable readouts. This review traces the transition from WCBs to natural consortia and engineered multicellular biosensors, emphasizing functional partitioning, signal routing, community control, and artificial intelligence (AI)-assisted design. AI is discussed as a practical tool for narrowing design space, predicting interactions, decoding complex biosignals, and supporting adaptive operation. Key challenges remain in community stability, orthogonal communication, data quality, biosafety, standardization, and real-sample validation. Future progress will depend on parsimonious community design, reliable containment, quantitative validation, and computational workflows that connect community composition with sensing performance.

Biosensors doi: 10.3390/bios16070365

Authors: Jun Duan Xinjie Guo Pinghua Sun Haibo Zhou Xiangjiu He

Seasonal epidemics and high variability of influenza viruses pose a severe threat to global public health security. Neuraminidase, a key functional enzyme in the life cycle of influenza viruses, represents an important target for anti-influenza drug development. Given the continuous emergence of drug-resistant strains against first-line clinical neuraminidase inhibitors (NAIs) such as oseltamivir, there is an urgent need to develop novel, broad-spectrum, and resistance-overcoming NAIs. Natural products, characterized by structural diversity and a wide range of biological activities, provide abundant resources for the discovery of new NAIs. Recent advances in computer-aided drug design, intelligent analytical platforms, and modern screening technologies have accelerated the identification of natural product-derived NAIs. In particular, biosensor-based strategies, including electrochemical, fluorescence, bioluminescence, and surface-enhanced Raman scattering biosensors, have demonstrated significant advantages in sensitivity, selectivity, rapid response, and high-throughput screening. In combination with computational methods and experimental approaches such as affinity ultrafiltration and activity-guided separation, these technologies have promoted the development of intelligent, precise, and multimodal screening platforms. Looking forward, the integration of biosensor-based high-throughput screening platforms with artificial intelligence algorithms is expected to drive the next generation of natural product screening platforms and facilitate the efficient discovery and clinical translation of novel NAIs. This paper systematically reviews the research progress of screening strategies for natural product-derived NAIs; introduces representative natural active NAIs, including phenols, terpenoids, and alkaloids; and prospects future development directions, aiming to provide a scientific reference for the efficient discovery of NAIs from natural products.

Biosensors doi: 10.3390/bios16070364

Authors: Yanger Liu Huifu Yuan Juan Zhang Xiaoyun Sun Peili Wang Pazilaiti Yiming Ailiang Chen Yanyang Xu

Accurately detecting viable foodborne pathogenic bacteria is essential for food safety risk assessments and public health interventions. Traditional plate counting is time-consuming and operationally cumbersome. Immunological assays are unable to distinguish viable from dead cells, whereas conventional nucleic acid amplification is often affected by residual DNA originating from dead bacteria. These limitations render conventional approaches inadequate for rapid and precise field detection. Functional nucleic acids (FNAs) offer a promising alternative for viability detection because of their high sensitivity, specificity, target diversity, and programmable integrability. This review provides a systematic overview of molecular recognition strategies and FNA-based detection technologies for identifying viable foodborne microorganisms. We categorize the biomarkers targeted by FNAs into nucleic acids, surface structures, and metabolic activities. Building on this categorization, we examine the core principles and technological evolution of primers, aptamers, DNAzymes, guide nucleic acids, and oligonucleotide probes in viability discrimination. We then outline the practical applications of these technologies across the food supply chain and discuss the remaining challenges and future directions in the field. Ultimately, this work provides a theoretical reference and practical guidance for ensuring food safety and advancing precise microbial risk management.

Biosensors doi: 10.3390/bios16070363

Authors: Bin Gu Weilong Tu Biao Zou Yuxian Chen Qiaolin Fan Cong Zhang Xiao Li Tao Hu

Neuropeptide Y (NPY) is a stress-modulating neuropeptide and a promising biomarker for non-invasive assessment. Herein, a sensitive electrochemical aptasensor was developed on reduced graphene oxide/gold nanoparticle (rGO/AuNP)-modified screen-printed electrodes for selective NPY detection. A methylene blue (MB)-labeled NPY-specific aptamer was immobilized on the electrode surface through Au–S chemistry, and square-wave voltammetry (SWV) was used for signal readout. The rGO/AuNP-modified interface provided high conductivity and a large effective surface area, facilitating electron transfer and probe immobilization. Under optimized conditions, the aptasensor exhibited a linear detection range of 10–10,000 pg mL−1 in PBS with a low detection limit of 1.17 pg mL−1 and good linearity (R2 = 0.991). In addition, the sensor showed satisfactory selectivity, reproducibility, and mechanical stability. Recovery tests in artificial sweat yielded recoveries of 91.8–107.8% with relative standard deviations below 5%, demonstrating good analytical accuracy in complex matrices. Combined with an agarose-hydrogel-assisted sampling interface and a reverse-iontophoresis-compatible wearable platform, this low-cost and facile sensing strategy provides a portable proof-of-concept approach for NPY analysis in artificial sweat and shows potential for future wearable-oriented biofluid monitoring.

Biosensors doi: 10.3390/bios16070362

Authors: Mauricio Barramuño-Medina Pablo Valdés-Badilla Pablo Aravena-Sagardia Jordan Hernandez-Martínez Edgar Vásquez-Carrasco Tatiana Romero-Arias Claudio Bascour-Sandoval Germán Gálvez-García

Post-error slowing (PES) is commonly considered a behavioral marker of post-error adaptation. However, adaptive processes may also emerge through subtle modifications of motor preparation, particularly in combat sports such as taekwondo (TKD), where maintaining rapid motor execution is essential. This study examined post-error neuromuscular adjustments during a TKD-specific kicking task by comparing standard Go and post-error Go trials for changes in muscle onset latency, peak electromyographic amplitude, and co-contraction indices. Twenty-eight TKD athletes (14 novice and 14 advanced) performed a sport-specific Go/No-Go task while wearable wireless surface electromyography sensors recorded lower-limb neuromuscular activity from eight lower-limb muscles. Muscle onset latency, peak electromyographic amplitude, co-contraction indices, and reaction time were analyzed using linear mixed-effects models. Post-error Go trials showed significant alterations in muscle onset latency in posterior lower-limb muscles involved in propulsion and movement preparation (semitendinosus, biceps femoris, lateral gastrocnemius, and soleus), with muscle activation occurring closer to the foot take-off. No significant differences were observed in reaction time, peak electromyographic amplitude, or co-contraction indices, and expertise and age did not modulate these effects. These findings suggest that error-related motor adjustments may be expressed through changes in muscle activation timing rather than overt behavioral slowing.

Biosensors doi: 10.3390/bios16070361

Authors: Kornanong Yuenyongchaiwat Lucksanaporn Mahawong Chaopraya Nenmanee Sasipa Buranapuntalug Chusak Thanawattano

Incentive spirometry is widely used in respiratory rehabilitation to enhance lung expansion and prevent postoperative pulmonary complications. However, conventional devices, including volume-oriented and flow-oriented incentive spirometers, rely on subjective visual interpretation, which may limit measurement accuracy and clinical utility. A digital incentive spirometer (DIS) has been developed to provide objective, real-time measurements of inspiratory volume. This study aimed to evaluate the agreement and reliability between the DIS and a volume-oriented incentive spirometer (VIS) across different age groups. A cross-sectional study was conducted in 150 participants aged 7–80 years, stratified into five age groups with equal sex distribution. Inspiratory volume was measured simultaneously using both devices. Agreement was assessed using Bland–Altman analysis, and reliability was evaluated using intraclass correlation coefficients (ICC). The DIS demonstrated good overall reliability (ICC = 0.868, 95% CI: 0.821–0.903). The mean difference was 48.69 mL, indicating slight overestimation by the DIS. However, the limits of agreement were wide (−469.24 to 566.63 mL), suggesting limited interchangeability. Reliability varied across age groups, with the highest ICC in older adults and the lowest in adolescents. The DIS showed good reliability but limited agreement with the VIS.

Biosensors doi: 10.3390/bios16070360

Authors: Paweł Stańczak Maciej Trzaskowski Mariusz Pietrzak

Nanozymes, nanomaterials that mimic enzymatic activity, offer superior stability, tunability, and lower production costs compared to natural enzymes. To date, most nanozyme-based point-of-care (PoC) diagnostic systems have relied on oxidation reactions, such as oxidation of 3,3′,5,5′-tetramethylbenzidine, which often suffer from limited substrate stability and high background signal. This study investigates reduction reactions, particularly those involving tetrazolium salts, as an alternative route for signal generation in PoC devices. For this purpose, monometallic and bimetallic gold, palladium, and platinum nanoparticles were synthesized via chemical reduction using poly(vinyl alcohol) as a stabilizing agent. The resulting nanoparticles were uniform in size and morphology. Their catalytic performance was confirmed through the reduction of 4-nitrophenol. The tetrazole salts were selected as promising substrates for application in PoC settings and further explored by examining the nanozyme-based reduction of 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT). The nanozymes catalyzed the reduction of MTT in the presence of sodium borohydride, producing a distinct colorimetric signal under selected conditions. The effects of reducing agent concentration, buffer pH, and potential interferents were evaluated, with performance suitable for PoC devices achieved at basic pH and low borohydride concentration. Interference studies showed negligible MTT reduction in the presence of physiological levels of ascorbic acid, human serum albumin, and 10% concentration of human serum. Finally, a proof-of-concept lateral flow assay demonstrated successful signal generation through nanozyme-catalyzed MTT reduction. Results establish tetrazolium salts as suitable substrates for nanozyme-enhanced PoC diagnostics and highlight reduction-based chromogenic systems as a viable alternative to traditional oxidation-based assays.

Biosensors doi: 10.3390/bios16070359

Authors: Meiqing Yang Qiuhe Hu Suiping Wang Haozi Lu Song Liu

The rapid and sensitive quantification of antibiotic residues in aquatic animals is crucial for ensuring food safety and protecting public health. Electrochemical (bio)sensors show great potential in this field due to their quick response time, low cost, and ease of miniaturization. This paper presents a systematic review of advances in the electrochemical detection of eight classes of antibiotics: fluoroquinolones, sulfonamides, amphenicols, tetracyclines, nitrofurans, macrolides, aminoglycosides, and β-lactams in aquatic animal samples. It covers four types of sensors: direct electrochemical sensors, immunosensors, aptasensors, and molecularly imprinted sensors. The review emphasizes the electrochemical behavior of the targets, interface design, recognition elements, signal amplification strategies, and validation using real samples. It also summarizes the sample pretreatment methods for different classes of antibiotics. Finally, the paper analyzes key challenges related to adaptability to complex matrices, consistency in sample preparation, and validation with real-world samples. Additionally, it proposes future directions for development in this field.

Biosensors doi: 10.3390/bios16070358

Authors: Mengdi Hou Gaoming He Zongchang Liu Jianbo Huang Heliang Zou

Compact malaria detectors for microcontrollers are almost always benchmarked on the NIH Malaria dataset with a per-cell random split. This leaks slide identity because the cells come from only about 200 slides and a random split mixes same-slide cells across training and testing. The leakage also distorts architectural conclusions: under a leakage-free slide-disjoint protocol, per-module ablation gains collapse to seed noise and an apparent cross-site robustness variant loses most of its advantage. Headline accuracy falls from 97.1% to 95.6%, a gap that sits within the cross-seed noise, and all eight tested architectures move the same way. The evidence is this unanimous direction, not the size of any single gap. This benchmarking finding is our main contribution. Two results survive. First, MalariaNet, our 21 K-parameter detector, reaches about 95.6% accuracy at 23.5 KB of INT8 weights, with a numerically faithful on-chip forward on an STM32H743 at a 1.2 FPS triage rate. Second, it is among the most interference-robust of the eight networks and the most robust microcontroller-deployable model. Scope is limited to single P. falciparum thin-smear cells. Slide-disjoint evaluation should become standard, and we provide MalariaNet as the first leakage-free, on-device-validated point-of-care malaria reference.

Biosensors doi: 10.3390/bios16070357

Authors: Jian Xue Wenli Fu Luhang Liu Qizhong Qin Jieying Gao Yingli Li Anyi Chen

A simple and sensitive fluorescence sensing method was developed for ciprofloxacin (CIP) determination based on manganese-doped carbon dots (Mn-CDs). The Mn-CDs were synthesized through a one-step hydrothermal method using anhydrous citric acid and manganese chloride tetrahydrate as precursors. The prepared Mn-CDs exhibited good dispersibility, uniform nanoscale morphology, abundant surface functional groups and favorable fluorescence properties. The incorporation of Mn was designed to introduce coordination-related binding sites for CIP, thereby enhancing the interaction between Mn-CDs and CIP. Under excitation at 330 nm, the Mn-CDs showed a pronounced fluorescence enhancement response toward CIP, enabling their use as fluorescent probes for quantitative detection. Under the optimized conditions, the fluorescence intensity increased linearly with CIP concentration over the range of 20 nM–10 μM, with a detection limit of 1.12 nM. The proposed sensing system exhibited satisfactory selectivity toward CIP over various potentially interfering substances and good storage stability. The practicality of the method was further verified by analysis of pond water samples, affording recoveries of 86–118% with relative standard deviations below 5%. In addition, the method showed acceptable applicability for CIP determination in different pharmaceutical formulations. These results indicate that the Mn-CD-based fluorescent probe provides a convenient, sensitive and promising platform for CIP determination in environmental and pharmaceutical samples.

Biosensors doi: 10.3390/bios16070356

Authors: Qian Wang Huiyao Liang Xiaofeng Chang Huili He Rong Li Jian Mao Weiwei Han Ying Tang Yongfei Li Maogang Li Qunzheng Zhang

Carbon dots (CDs) have been widely employed in diverse fields by virtue of their excellent water solubility, low toxicity, high fluorescence stability, and favorable biocompatibility. Nevertheless, traditional preparation methods for CDs generally suffer from drawbacks that run counter to the concept of green chemistry. This review comprehensively summarizes the green synthesis technologies, machine learning (ML)-assisted synthesis strategies, and diversified application fields of fluorescent CDs. Specifically, it discusses the characteristics of synthetic organic molecular/polymeric materials and natural sources (e.g., plants and fruit peels, etc.) and elaborates on the top-down and bottom-up green synthesis methods, analyzing their advantages. It also focuses on ML’s core role in precisely regulating CD emission wavelengths, enhancing and predicting fluorescence quantum yields to optimize synthesis processes. Additionally, this review highlights the representative biological applications of CDs, including biosensing and biomedicine (e.g., bioimaging, drug delivery, and photodynamic therapy), while briefly covering their applications in other fields. Finally, the review points out current challenges in green synthesis, ML-assisted applications and industrial translation, and puts forward future research directions, aiming to promote the greenization, intellectualization and large-scale development of CDs.

Biosensors doi: 10.3390/bios16070355

Authors: Derick Yongabi Alex Krane Heloisa Espreafico Guelerman Ramos Sofia Xavier Bustia Jonas Gruber Michael J. Schöning Frank Delvigne Patrick Wagner

Rapid, low-cost ethanol quantification is vital for beverage quality control, biofuel production, and pharmaceutical applications, yet current approaches are costly, reagent- or label-dependent, or rely on spectroscopy with substantial sample preparation. We introduce a purely cell-based, label-free biosensor that exploits temperature-gradient-induced spontaneous detachment of Saccharomyces cerevisiae from a chip surface. The readout is the detachment half-time, td50, derived from time-resolved changes in interfacial thermal resistance, Rth, at the solid–liquid interface. Cells were pre-exposed to ethanol (0–70% v/v) and the detachment kinetics monitored using the heat transfer method (HTM). Under these conditions, cells display a pronounced non-monotonic td50 response with a peak around 20% v/v ethanol. Overall, the td50 rises from ~45 min (0% ethanol) to ≳10 h (20%) and then decreases, with no detachment at 60% and beyond. Critically, cell quality gates the detachment window. Fresh yeast responds up to ~50%, whereas aged yeast ceases to detach above ~8%, demonstrating a dual-function assay. Complementary measurements show that ethanol decreases surface tension monotonically, as expected, while optical/SEM imaging reveals aggregation above the detachment window. Requiring only a heater and a temperature probe, this platform offers a compact and low-cost strategy for ethanol sensing. Its applicability in a complex matrix is further demonstrated using whiskey diluted to selected alcohol concentrations, which produced responses consistent with the ethanol calibration trend. Potentially, it also offers a thermal assay for real-time monitoring of microbial cell quality across biotechnology and bioengineering applications. Considering ethanol as a proxy for drugs, the strategy may also support label-free drug screening on cells. At a fundamental level, the non-monotonic effect of ethanol, and especially the sharp maximum at 20%, remains unresolved and invites further studies.

Biosensors doi: 10.3390/bios16070354

Authors: Zulaikha Fatima Abdullah Nida Hafeez Rolando Quintero Téllez Miguel Jesús Torres Ruiz Carlos Guzmán Sánchez Mejorada Miguel Félix Mata-Rivera Roberto Zagal-Flores

As global populations grow and technology advances, daily life is increasingly shaped by digital systems such as computers and smart devices. However, prolonged device use has contributed to increasing physical and mental health concerns, particularly those associated with poor sitting posture. Posture-related strain is frequently overlooked and contributes to musculoskeletal discomfort, including back, neck, shoulder, and wrist pain, and may also be associated with sleep disturbances and elevated stress levels. To the best of our knowledge and based on the existing literature, this is the first study to introduce a machine learning-based framework for advanced muscle strain severity classification using Internet of Things (IoT) devices that integrates posture monitoring and muscle strain detection into a unified low-cost framework ($23 hardware cost). The primary objective of this work is accurate classification of muscle strain severity, while real-time alerts serve as a secondary ergonomic feedback mechanism. Specifically, this study makes four major contributions. First, we created a novel dataset through real-time acquisition of electromyography (EMG) and posture signals from participants in hospital and industrial environments, capturing diverse muscle strain patterns validated against clinical assessment procedures. Second, we designed a two-part hardware architecture consisting of posture detection (PD) and strain detection (SD) modules using a NodeMCU ESP8266, HC-SR04 ultrasonic sensor, EMG sensor, and buzzer for real-time physiological monitoring, incorporating EMG-specific preprocessing including band-pass filtering, rectification, and RMS smoothing. Third, we proposed and evaluated a hybrid machine learning framework integrating Vision Transformer (ViT) and XGBoost to classify strain severity into three study-specific categories: baseline (EMG RMS < 40 µV), compensatory strain (40–59 µV), and overload (≥60 µV). These categories were used as reproducible severity proxies for machine learning annotation and should not be interpreted as universal biomarkers of structural tissue damage. Finally, the proposed framework achieved a classification accuracy of 99.0% (95% CI: 98.5–99.5%) with an inference latency of 15.2 ms.

Biosensors doi: 10.3390/bios16070353

Authors: Izabela Zaras Piotr Pieta Marta Jarczewska

Gold is considered the most widely used surface for the development of aptamer-based layers. However, its high cost, laborious surface-cleaning protocols, and susceptibility of receptor layers to degradation in complex samples, including biological fluids, enforce the search for alternative transducers. One solution is the application of carbon materials, which are inexpensive and allow for the use of a wide potential range when electrochemical measurements are performed. Herein, we present studies on the elaboration of aptamer receptor layers formed on carbon macroelectrodes. To achieve this, a one-step procedure for aptamer molecules containing a pyrene or anthracene group at the 5′ end was used, with immobilization via adsorption facilitated by Π–Π interactions between the anchor group and the carbon surface. It was evidenced that using anthracene-modified aptamer and sodium anthraquinone-2-sulfonic acid (AQMS) redox indicator enabled the detection of a model analyte–vancomycin below the millimolar concentration range. It was also revealed that vancomycin can be successfully detected in serum samples, and the aptasensor exhibits good selectivity towards vancomycin. The latter was observed by comparison of responses in PBS containing solely vancomycin and a solution spiked with vancomycin and a mixture of antibiotics.

Biosensors doi: 10.3390/bios16070352

Authors: Kundan Kumar Mishra Shanmathi Venkatesan Sriram Muthukumar Shalini Prasad

Food contamination by Aflatoxin M1 and Atrazine remains a critical food-safety concern, requiring sensitive detection methods that can operate reliably in complex matrices. Here, we report an AI-assisted antibody-functionalized electrochemical sensing platform for the detection and classification of Aflatoxin M1 and Atrazine across corn, corn flour, and protein matrices. The sensor used analyte-specific antibodies immobilized on an electrochemical electrode surface, where target binding produced measurable changes in the interfacial electrochemical response. Sensor performance was evaluated using cyclic voltammetry, coulometry, and electrochemical impedance spectroscopy (EIS), with EIS providing strong frequency-dependent signatures for concentration-dependent analysis. Spike-and-recovery studies further demonstrated the applicability of the platform in food-matrix conditions. To improve interpretation of complex electrochemical signals, full-spectrum EIS features were integrated with machine learning models for concentration-level classification into low, mid, and high groups. The AI workflow achieved an overall classification accuracy of 93.33%, with 96.67% specificity, 93.44% PPV, 96.66% NPV, and 0.982 AUC for Atrazine, and 96.70% specificity, 93.38% PPV, 96.67% NPV, and 0.987 AUC for Aflatoxin M1. In addition, analyte classification between Aflatoxin M1 and Atrazine reached 97.4% accuracy and 0.994 ROC-AUC. Overall, this work demonstrates a matrix-aware electrochemical immunosensing strategy enhanced by AI-based signal interpretation for food contaminant detection.

Biosensors doi: 10.3390/bios16060351

Authors: Mingjie Wei Linxin Zheng Weilong Tian Xingfeng Wang Rong Liu Lijuan Chen Li Niu

Copper ions (Cu2+) and intracellular biothiols are tightly coupled in cellular redox regulation, where copper–thiol coordination governs oxidative stress and metal homeostasis. However, analytical platforms capable of sequentially monitoring Cu2+ and biothiols within a single molecular system remain scarce. Herein, we report a coumarin-based fluorescent probe XDP that enables sequential ON–OFF–ON sensing of Cu2+ and biothiols through a coordination–competition mechanism. The imine (C=N) site of XDP selectively coordinates Cu2+, leading to fluorescence quenching arising from coordination-induced electronic perturbation and enhanced nonradiative decay. The probe exhibits a linear response toward Cu2+ over 1–80 μM with a detection limit of 0.108 μM. Subsequent competitive binding of biothiols (GSH, Cys, and Hcy) releases Cu2+ from the complex, thereby restoring fluorescence and enabling detection within 1–30 μM with submicromolar sensitivity. XDP also displays a large Stokes shift (135 nm), which minimizes spectral overlap and improves signal reliability. Notably, Cu2+ binding triggers a distinct color change that supports naked-eye detection and smartphone-based RGB quantification. The probe further enables visualization of Cu2+ and thiol-triggered signal recovery in living cells and zebrafish. This work establishes a versatile analytical platform for probing copper–thiol interactions in environmental and biological systems.

Biosensors doi: 10.3390/bios16060350

Authors: Sergio Espinoza-Torres Astrid Choquehuanca-Azaña Nathalia Florencia B. Azeredo Marcos Rufino Lucio Angnes

Molecularly imprinted polymers (MIPs) offer robust, cost-effective, and highly selective alternatives to fragile biological receptors. Specifically, electropolymerization has emerged as a versatile strategy that enables the precise, in situ formation of uniform MIP films directly on electrode surfaces. This review provides a comprehensive overview of electropolymerized MIPs (eMIPs) supported on advanced carbon-based materials for electrochemical (bio)sensing. We emphasize how the synergistic integration of eMIPs with carbonaceous architectures significantly enhances electron transfer, active surface area, and overall analytical sensitivity. Key fabrication aspects are systematically discussed, including monomer selection, electropolymerization parameters, and efficient template removal. A central aspect of this work is the critical categorization of sensing mechanisms into direct and indirect detection strategies. This distinction elucidates how eMIPs can quantify a broad spectrum of electroactive and non-electroactive targets in complex matrices, while strategically avoiding excessively high applied potentials. Finally, alongside outlining the transition of these systems into portable technologies, we address a critical shortcoming in the current literature: the urgent need for analytical standardization through the rigorous reporting of Imprinting and Selectivity Factors using Non-Imprinted Polymer (NIP) controls.

Biosensors doi: 10.3390/bios16060349

Authors: Yaliang Huang Ning Wang Xinyao Yi Ning Xia

Alzheimer’s disease (AD) has become a neurodegenerative disease with an increasing incidence rate and a large economic and social burden worldwide. Amyloid-beta oligomer (AβO) has been confirmed as a key neurotoxic species and a core diagnostic biomarker in AD. Traditional methods for AβO detection have drawbacks, such as cumbersome operation, high cost, and dependence on sophisticated instruments, hindering their transformation into fast and real-time detection techniques. (Photo)electrochemical biosensors have attracted much attention due to their inherent advantages, such as high sensitivity, low cost, portability, and ease of miniaturization. This review systematically summarizes the latest progress of (photo)electrochemical biosensors for AβO detection, mainly based on two sensing modes: direct detection and sandwich-type detection. We comprehensively elaborated on the sensing performances and recognition elements, such as antibodies, aptamers, peptides, and molecularly imprinted polymers. The integration of functional nanomaterials and signal amplification strategies was emphasized to improve the sensitivity, selectivity, and stability of biosensors. In addition, we discussed the existing challenges and looked forward to the future development direction for the early diagnosis of AD. This article aims to provide a systematic reference for the rational design and practical application of advanced biosensors in biomarker detection and AD-related precision medicine.

Biosensors doi: 10.3390/bios16060348

Authors: Jovana Arsenović Alisa Budak Melinda Taši Mladena Lalić-Popović Nemanja Todorović Maja Milanović Nataša Milić Nataša Milošević

Epilepsy can be effectively controlled with appropriately selected antiepileptic drugs and carefully titrated dosage regimens. Although lamotrigine exhibits favorable pharmacokinetic properties following oral administration, fluctuations in plasma concentration may still occur due to interindividual variability, irregular dosing, and pharmacokinetic interactions. In this study, a subcutaneous implant capable of monitoring plasma lamotrigine levels and adjusting drug delivery accordingly was developed to maintain stable therapeutic concentrations. The proposed system combines intermittent drug release with continuous concentration monitoring using an enzymatic biosensor. A pharmacokinetic model based on first-order absorption and elimination kinetics was implemented in MATLAB/Simulink using clinical lamotrigine concentration data obtained from patients receiving chronic therapy. In the closed-loop configuration, biosensor measurements were used as feedback for a proportional–integral (PI) controller that adjusted the implant release rate in real time. System performance was evaluated using in silico simulations. The open-loop system produced rapid concentration peaks (Cmax ≈ 0.06 mmol/L) followed by a decline below the therapeutic threshold within approximately 80 min. In contrast, the closed-loop system achieved lower peak concentrations (Cmax ≈ 0.045 mmol/L) and maintained plasma concentrations within the therapeutic range of 0.02–0.03 mmol/L with reduced fluctuations. These findings support further investigation of biosensor-guided closed-loop lamotrigine delivery systems.

Biosensors doi: 10.3390/bios16060347

Authors: Diogo Serrano Paulo Zoio Luís P. Fonseca Cecília R. C. Calado

The development of new technologies enabling rapid, frequent, and reagent-free monitoring of kidney function is recognized as being of paramount importance. In this work, mid-(MIR) and near-infrared (NIR) spectroscopy were compared for the prediction of key renal biomarkers—creatinine, urea and albumin—using 54 serum solutions mimicking the biochemical profiles of five stages of chronic kidney disease (CKD). MIR spectra were acquired in a high-throughput microplate platform after a simple dehydration step, while the NIR spectra were obtained directly from liquid serum using a fiber optic probe. After evaluating several spectral pre-processing methods and targeted spectral regions, excellent regression models (R2 > 0.9 for the best models) were obtained for the three biomarkers. MIR provided highly accurate urea predictions, whereas optimized NIR sub-regions enabled excellent estimation of creatinine and albumin. Both MIR and NIR, associated with supervised classification methods, enabled us to successfully distinguish healthy from diseased profiles and to identify the diseases state with AUC > 0.93. These findings highlight the complementary value of MIR and NIR spectroscopy for kidney disease assessment and their potential integration into point-of-care diagnostic systems.

Biosensors doi: 10.3390/bios16060346

Authors: Jun Gyu Park Woohyun Park Suji Choi Sanghyo Lee Minseok Kim

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings.

Biosensors doi: 10.3390/bios16060345

Authors: Aigerim Dyussupova Aisha Ilyas Aigerim Boranova Yegor Shevchenko Xeniya Terzapulo Ansar Seitkali Abduzhappar Gaipov Olena Filchakova Rostislav Bukasov

Human saliva is a heterogeneous bodily fluid with a complex composition, which contains antibodies, proteins, and viruses, making it applicable in clinical diagnosis. There are several advantages of the analysis of saliva samples over other biofluids, including a non-invasive and simple collection procedure for extraoral detection. Biomarker or pathogen detection in saliva can be performed with various methods: mass spectrometry, PCR, ELISA, electrochemical, and optical methods such as fluorescence, SPR, and SERS. The early detection of cancer and other disease biomarkers, as well as infectious agents, can be crucial for effective treatment and minimization of mortality from those diseases. The following paper reviews extraoral detection techniques to identify the most sensitive methods for diagnosing early and asymptomatic patients. The LODs collected and tabulated from 149 analytical papers, alongside the sensitivity, specificity, and sometimes the area under the curve (AUC) tabulated from 118 clinical studies, have all become parameters for the comparative quantitative analysis. Based on the limited but substantial number of analytical studies on the detection of cortisol in saliva (29), the electrochemical platforms demonstrated the highest sensitivity, with a geometric mean LOD of 11 pM. Within these methods, voltametric ones showed the best performance with 6 pM geometric mean LOD. Electrochemical techniques are then followed by immunoassay- and mass spectrometry-based platforms, with corresponding geometric average LOD values of 39.1 and 171 pM, respectively. However, clinical outcomes are at least as meaningful as LOD values. In terms of clinical analysis, ELISA and direct-SERS outperformed other methods, achieving balanced accuracy of approximately 87% and AUC values of 0.96 for direct SERS and 0.86 for ELISA. MS and PCR followed closely, with balanced accuracies around 84%. While the direct SERS is not yet widespread in clinical applications, its potential can be forged if the standardization issue is addressed.

Biosensors doi: 10.3390/bios16060344

Authors: Dongyoun Kim Syed Saad Ahmed Amirhossein Amjad Kwanghee Won Xiaojun Xian

Wearable healthcare technologies are transforming the healthcare landscape by enabling remote, real-time health data collection, supporting early diagnosis, personalizing treatment plans, and reducing healthcare costs and medical burdens. Central to these advancements are wearable sensors, which continuously capture physiological data such as heart rate, temperature, activity levels, and biomarker concentrations. However, the large volume and complexity of this data demand effective processing to extract meaningful medical insights. Artificial intelligence (AI) and machine learning (ML) have significantly enhanced the capabilities of wearable sensors by enabling advanced data analysis, pattern recognition, and predictive modeling. AI-enhanced wearable sensors can detect early signs of health issues, such as heart attacks, chronic diseases, and mental health conditions like stress, often before clinical symptoms become apparent. This review examines the integration of AI/ML models with wearable sensors across physical activity recognition, stress assessment, cardiovascular monitoring, personal exposure monitoring, and sweat biomarker detection. Unlike prior application-centered reviews, we emphasize methodological and translational evaluation by comparing task formulations, sensing modalities, dataset scale, validation protocols, performance metrics, and deployment constraints across domains. We further discuss advanced architectures, multimodal fusion, explainable AI, edge deployment, privacy and regulatory considerations, and the translational gap between research prototypes and clinically deployable wearable AI systems.

Biosensors doi: 10.3390/bios16060343

Authors: Marya K. Kolokolova Liliya I. Mukhametova Boris S. Tupertsev Anatoly V. Zherdev Xinxin Xu Chuanlai Xu Sergei A. Eremin

4-Chlorophenoxyacetic acid (4-CPA), a synthetic auxin analog, is employed in agriculture both as a plant growth regulator and as a constituent of herbicide formulations. Consequently, the establishment of simple and rapid detection methods is essential for effective environmental monitoring. This study reports the first development of a homogeneous fluorescence polarization immunoassay (FPIA) for the determination of 4-CPA. The monoclonal antibody (M1), raised against 4-CPA, was evaluated as a recognition element. Furthermore, two fluorescently labeled 4-CPA tracers—with ethylenediamine fluorescein thiocarbamate and aminohexylaminocarbonylfluorescein—were synthesized and purified, and their structures were unequivocally confirmed by high-performance liquid chromatography coupled with high-resolution mass spectrometric detection (HPLC-HRMS). Optimal concentrations of monoclonal antibodies and tracers were established, yielding a limit of detection of 1.2 ng/mL. The assay demonstrated a broad dynamic range of 2.3–300 ng/mL and a rapid analysis time of 15 min. Validation via the standard addition method in authentic open water samples resulted in recovery rates of 98–112%. To address the cross-reactivity with the prevalent herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), two novel strategies were devised and successfully implemented. The first approach involves the concurrent execution of two separate FPIAs—one for 2,4-D and one for 4-CPA—followed by the mathematical resolution of two analyte concentrations from the two measured binding values. The second strategy entails the preliminary selective removal of 2,4-D from sample matrices using affinity chromatography columns with immobilized anti-2,4-D antibodies prior to FPIA for 4-CPA. These proposed methodologies appear highly promising for overcoming the inherent limitations of traditional immunoassays when faced with significant cross-reactivity among structurally analogous compounds.

Biosensors doi: 10.3390/bios16060342

Authors: Jiaqi Chen Xingtian Wei Yihao Shi Yang Piao Jiakang He Hailan Chen Jincheng Xiong Lilan Lyu Liang Luo

Chemical contaminants in food pose a serious threat to public health, driving the need for sensitive, rapid, and on-site screening methods. Lateral flow immunoassay (LFIA) is rapid and portable but suffers from single-signal readout and insufficient label stability. Nanozymes, nanomaterials with enzyme-like catalytic activity and excellent stability, have emerged as promising signal labels to address these limitations. Moreover, their diverse physiochemical properties enable multiplex signal readout, where two or more complementary signals (e.g., colorimetric, fluorescent, chemiluminescent, photothermal, and surface-enhanced Raman scattering) are generated simultaneously from a single test line. This multiplex strategy significantly enhances detection sensitivity, accuracy, and reliability through signal amplification and self-calibration. This review provides a systematic overview of the catalytic properties and their major types used in multiplex signal LFIA. The signal combination strategies employed in nanozyme-based multiplex signal LFIA were also summarized, and their applications in detecting veterinary drugs, mycotoxins, pesticides, and other food chemical contaminants are highlighted. Ultimately, current challenges and future prospectives in this field are discussed. This review offers guidance for designing high-performance, nanozyme-based multiplex signal LFIA platforms for food safety monitoring.

Biosensors doi: 10.3390/bios16060341

Authors: Gil Zimran Assaf Mosquna

Genetically encoded biosensors are now central tools, deployed either as intracellular reporters to advance basic research, or as whole-cell reagents that detect analytes in diverse sample-types. Across the diversity of molecular scaffolds and modes of operation, biosensors serve a common functional purpose: translating ligand presence into a readable signal. Despite this shared logic, biosensor development as a field of practice remains fragmented: different scaffolds and modalities are advanced in separate, often lab-specific pipelines with diverse assays, metrics, and design practices. Moreover, libraries, selection histories and performance data generated during routine campaigns rarely outlive the projects that produced them. In this perspective, we focus on this fragmentation as a field-level bottleneck and argue that it deserves explicit attention in its own right. We discuss how modest, incremental steps—such as structured development records, adherence to high-information screening formats, library annotation, and community-level deposition infrastructure—could make biosensor development more reproducible, more comparable, and easier to build on across projects and laboratories. We further argue that such infrastructure will become increasingly valuable as computational protein design matures—not as a competing approach, but as the source of diverse, comparable, and context-annotated experimental data that sequence-function models and design benchmarks ultimately depend on.

Biosensors doi: 10.3390/bios16060340

Authors: Simone Fortunati Shaista Nazir Federico Biondi Mattia Amariglio Eloisa Tosi Roberto Corradini Gaetano Donofrio Francesca Lambertini Michele Suman Alex Manicardi Marco Giannetto Maria Careri

Food allergies are one of the most critical food safety issues, with epidemiological studies confirming a global increase. In this context, effective and sensitive analytical methods play a crucial role in ensuring allergen-free food products. To face this issue, electrochemical biosensors offer powerful, sensitive, selective, and cost-effective alternatives to conventional methods for food allergen analysis while enabling rapid on-site detection. In this study, we developed a sandwich electrochemical magneto-genoassay aimed at the parallel detection of soy (Glycine max) and mustard (Sinapis alba) allergens, suitable for implementation on multichannel instrumentation. The assay involves the functionalization of magnetic microbeads functionalized with peptide nucleic acid-based (PNA) capture probes, capable of undergoing target-induced bio-orthogonal ligation with biotin-labelled signalling probes. Carbon nanotubes-modified screen-printed carbon electrodes were exploited for the voltammetric readout. We demonstrated the effectiveness of functional PNA probes by comparing their performance with those achieved using analogous DNA probes. The developed method exhibited excellent selectivity in terms of cross-reactivity, sensitivity, and precision, achieving detection limits of 16 and 19 pM for soy and mustard, respectively. Finally, by successfully applying the biosensor platform to genomic DNA extracted from plant-based food ingredients, we demonstrated its potential as a valuable tool in food safety risk management.

Biosensors doi: 10.3390/bios16060339

Authors: Nihal Ermiş Nigar Aksöz Mustafa Oğuzhan Sert

Phenylephrine is a widely used α1-adrenergic agonist employed as a decongestant and vasoconstrictor in numerous pharmaceutical formulations. Considering its widespread use and its relevance in biological monitoring and anti-doping control, the development of rapid, sensitive, and reliable analytical methods for its determination has attracted significant attention. A paper-based colorimetric sensor based on Prussian blue nanoparticles was developed for the determination of phenylephrine. Prussian blue nanoparticles were synthesized by the precipitation method, and their structural, morphological, and surface properties were systematically characterized using complementary analytical techniques. The sensing mechanism is based on the reduction in Prussian blue to its colorless form in the presence of phenylephrine, resulting in a decrease in absorbance intensity. Under optimized conditions (pH 6.5 and 5 min incubation time), the colorimetric sensor exhibited a linear response toward phenylephrine over the concentration range of 5–150 µg mL−1, with a limit of detection of 1.56 µg mL−1 (R2 = 0.9986). The sensing system was further integrated into a paper-based platform, enabling visual detection of phenylephrine. Digital image analysis using ImageJ showed a linear response over 5–150 µg mL−1 (R2 = 0.9884) and a detection limit of 5.37 µg mL−1. The sensor’s practical applicability was validated using artificial urine samples, yielding recovery values of 95.87–97.5% and relative standard deviations of 1.15–2.13%. Unlike conventional methods requiring multi-step reactions, this study introduces, for the first time, a simple paper-based colorimetric sensor for phenylephrine detection based on the direct Prussian blue–Prussian white redox transition integrated with digital image analysis.

Biosensors doi: 10.3390/bios16060338

Authors: Yong Li Hao Lu Min Xiang Jianzhi Yang Binyi Su Fuzhi Cao

Optically Pumped Magnetometers (OPMs) have emerged as a promising technology for developing flexible, wearable magnetoencephalography (OPM-MEG) systems, offering high spatiotemporal resolution without the need for cryogenic cooling. However, their application to phase–amplitude coupling (PAC)-based neural decoding remains largely unexplored. Investigating their decoding performance is essential for evaluating the capability of OPM-MEG in characterizing complex neural dynamics and discriminating motor states. In this study, OPM-MEG was utilized to record brain activity during rest, motor imagery, and motor execution tasks. A two-stage temporal optimization strategy combining time-resolved PAC localization and the Kullback–Leibler modulation index (KL-MI) was employed to extract robust PAC features from low-frequency phase and high-frequency amplitude coupling. α–γ and θ–γ PAC features were subsequently fed into a multiclass linear discriminant analysis (LDA) classifier for motor state decoding, and compared against baseline band-power feature decoding performance. Experimental results demonstrate that PAC features derived from OPM-MEG significantly outperform the corresponding baseline band-power features in decoding performance. Notably, α–γ PAC features effectively discriminate among different motor states, achieving a balanced accuracy of 85.91% in 10-fold cross-validation. This performance significantly exceeds the 50% one-vs-rest chance level and outperforms θ–γ PAC features. These findings provide initial evidence for the feasibility of OPM-MEG in PAC-based motor state decoding and a preliminary case study for characterizing motor-related neural dynamics in a wearable MEG system.

Biosensors doi: 10.3390/bios16060337

Authors: Jianjun Kang Yuanchun Chen Zongcheng Shu Cuimin Wu Fang Ke

Acid phosphatase (ACP) is a clinically important enzyme whose early-stage detection is hindered by its extremely low abundance, nonspecific tissue distribution, and rapid loss of activity under conventional analytical conditions. Herein, we present a target-driven in situ nanozyme synthesis strategy that enables rapid and ultrasensitive point-of-care testing (POCT) of ACP. In this approach, ACP catalyzes the hydrolysis of L-ascorbic acid 2-phosphate sesquimagnesium (AAPS), producing ascorbic acid (AA). The generated AA partially reduces Fe3+ ions to Fe2+, thereby initiating alkaline co-precipitation and in situ formation of Fe3O4 nanoparticles. Polyvinylpyrrolidone (PVP) stabilizes the nanoparticles and preserves catalytic accessibility, while their intrinsic magnetism allows for efficient magnetic separation to eliminate matrix interference. The resulting Fe3O4@PVP nanozymes display pronounced peroxidase-like activity, catalyzing hydrogen-peroxide-mediated oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB). Quantitative readout can be achieved using either spectrophotometric analysis or smartphone imaging. The sensing platform achieves a detection limit of 0.021 U/L within 40 min and demonstrates excellent sensitivity, selectivity, and operational robustness. Successful validation in human serum confirms its clinical feasibility, while smartphone-based imaging enables portable and low-cost quantification suitable for decentralized diagnostics. Collectively, this work establishes a generalizable paradigm for target-triggered nanozyme generation aimed at detecting low-abundance and labile biomarkers.

Biosensors doi: 10.3390/bios16060336

Authors: Rajib Mondal Manob Jyoti Saikia

Chronic diseases such as cardiovascular disorders, diabetes, neurological conditions, and kidney disease continue to rise worldwide. These conditions create a growing demand for continuous, non-invasive, and personalized health monitoring technologies. Wearable biosensors meet this need by enabling real-time physiological and biochemical measurements outside traditional clinical settings. Among wearable biosensors, those based on biofluids like sweat, tears, and saliva provide a painless alternative to blood sampling. These fluids also grant access to metabolites, electrolytes, hormones, proteins, and disease related biomarkers that reflect systemic health status. Advanced sensing technology allow us to continuously track health status by analyzing key biomarkers in these accessible biofluids. This review summarizes recent advances in non-invasive wearable biosensors and focuses on their sensing principles which includes biorecognition elements, signal transduction mechanisms, and data acquisition strategies. We also discussed key sensing modalities, including electrochemical, optical, thermal, and piezoelectric approaches, highlighting their advantages for wearable integration and performance in biofluid sensing. Finally the review also outlines recent developments and applications of these systems in biofluid sensing. In the end we highlights existing challenges, potential solutions, and future directions toward clinically deployable, AI-assisted precision healthcare systems.

Biosensors doi: 10.3390/bios16060335

Authors: Kaijie Zheng Chenjie Yan Mengwei Jiang Jing Lei Chengcheng Wang Kai Zhao Dajing Chen Min Guo

Traditional solid-contact ion-selective electrodes (SC-ISEs) are severely constrained by a long-standing thermodynamic bottleneck, which requires hours of pre-conditioning and stabilization to establish a stable phase-boundary potential. To fundamentally bypass this limitation, we present a paradigm shift in electrochemical ion sensing that exploits dynamic kinetics rather than waiting for thermodynamic equilibrium. In this paper, we report a transient potential profiling method that eliminates the need for equilibration by analyzing the open-circuit voltage decay during the first 60 s of polarization. A discharge step on indicator electrode returns the membrane to a reproducible initial state, allowing for the extraction of a concentration correlated coefficient. Using a calcium ISE with an optimized membrane, the early-stage polarization dynamics were fitted to a single exponential saturation model, predicting the steady state response with an average error of 1.6%. The method achieved high repeatability (intra-day RSD 3.22%), batch to batch reproducibility (4.57%), and recovery rates from 90.7% to 115.0% in real water samples. Validation against ion chromatography showed high agreement (R2 = 0.997). This strategy enabled conditioning free, disposable ISEs for point of care and environmental monitoring.

Biosensors doi: 10.3390/bios16060334

Authors: Sofia Pessanha João Miguel Silveira Paulo Ribeiro António Mata Valentina Vassilenko Sofia Barbosa

Early detection of dental caries remains a significant clinical challenge, as conventional diagnostic methods lack sensitivity for incipient lesions. Raman spectroscopy offers high chemical specificity for enamel characterization; however, clinical translation is hindered by the complexity of conventional polarized confocal systems. In this work, we present a Raman-based fiber-optic sensing approach for the detection and classification of dental enamel conditions, including sound, affected, and carious tissues. A custom fiber-optic probe was developed for remote measurements and evaluated against a reference polarized confocal Raman system. In addition to spectral discrimination, key factors affecting sensing performance were investigated, including spatial variability across enamel surfaces and angular sensitivity due to probe misalignment. Raman-derived features (carbonate-to-phosphate ratio, phosphate peak intensity, position, and bandwidth) were analyzed using a multinomial logistic regression classifier. The fiber-optic sensor achieved an overall classification accuracy of 73% (F1-scores: 0.55 sound, 0.63 affected, 0.9 carious), confirmed by leave-one-tooth-out cross-validation. Probe misalignment studies revealed robustness up to 10° angular deviation. These results demonstrate that a simplified non-polarized fiber-optic Raman system provides competitive diagnostic performance and clinically relevant robustness, supporting its development as a point-of-care sensing platform for early dental caries detection.

Biosensors doi: 10.3390/bios16060333

Authors: Jiajia Wang Xinyu Yuan Jianchao Zhang Jingyi Che Xiaojun Wang

As the primary peripheral relay station for vibrissal tactile information, the trigeminal ganglion (TG) features heterogeneous three-dimensional (3D) cytoarchitecture that eludes full characterization using conventional two-dimensional methodologies. A high-resolution 3D imaging and reconstruction pipeline is thus required to unveil TG structural organization and define the spatial framework of target-related sensory neurons. Herein, we established a fluorescence micro-optical sectioning tomography (fMOST)-based workflow for 3D cytoarchitectural mapping of TG anatomy and validated its utility for profiling the distributions of TG neurons innervating vibrissae via single-axon tracing. fMOST imaging coupled with propidium iodide (PI) staining was applied to acquire whole-head anatomical data encompassing the vibrissae and the TG at cellular resolution. Based on clearly resolved cellular morphology and the spatial distribution of neuronal somata, we delineated the soma distribution of TG neurons and revealed a spatially heterogeneous 3D organization pattern, from which we operationally defined two anatomically distinct subdomains: the neuronal soma-rich region (NSRR) and the fiber-rich region (FRR). Furthermore, with retrograde viral/genetic labeling combined with neuronal tracing, TG neurons innervating the C2, D3, and δ vibrissae were observed in both NSRR and FRR, showing partially overlapping yet spatially biased distributions consistent with previous population-level observations of vibrissa-row-dependent topography. Notably, TG neurons innervating the δ vibrissa occupied a comparatively broader spatial extent along the anteroposterior plane in our dataset. Overall, this study facilitates an in-depth mechanistic and anatomical understanding of TG cytoarchitectural organization and underlying functional mechanisms.

Biosensors doi: 10.3390/bios16060332

Authors: Petia Genova-Kalou Evdokiya O. Hikova Todor Kereziev Petar T. Kolev Vihar Mankov Hristo Kisov Anna Atanasova Georgi L. Dyankov

Surface plasmon resonance (SPR) is a key tool for quantifying biomolecular interactions, and its use in studying interacting components outside cellular systems is well-established. Over the past 20–25 years, cell-based SPR techniques have emerged, with the promise of precise detection of molecular interactions within their normal physiological environment. Research on a wide variety of biological samples, which requires the detection of numerous parameters, has led to the development of a broad range of SPR techniques. This review aims to trace the chronological development of these techniques and the factors that have driven them. In this context, particular focus is given to grating-coupled SPR applied to cell assays. Its specific capabilities are examined, and the respective advantages and disadvantages of other SPR techniques are discussed based on the results obtained from studying specific biological objects. Finally, we venture to predict the promising SPR techniques, as well as the areas of application in which significant results can be expected.

Biosensors doi: 10.3390/bios16060331

Authors: Arijit Patra Lunden Melton Lenwood S. Sawyer Tate King Sujay Ray

Single-molecule Förster resonance energy transfer (smFRET) enables direct measurement of nanoscale conformational dynamics and heterogeneity in biomolecules, but quantitative interpretation of smFRET data critically depends on well-controlled excitation geometry, low background fluorescence, robust calibration, and reproducible data-analysis workflows. Prism-based total internal reflection fluorescence (pTIRF) microscopy provides important advantages for such measurements by physically separating excitation and emission paths and generating a highly confined evanescent field, yet practical guidance for implementing reproducible, quantitative pTIRF systems remains fragmented. Here we present a comprehensive, standardized framework for the design, alignment, calibration, validation, and operation of a prism-based TIRF microscope optimized for single-molecule fluorescence measurements. We describe the complete optical architecture for dual-color excitation and detection, establish alignment invariants that ensure reproducible evanescent excitation and stable donor–acceptor channel registration, and detail surface preparation, flow control, and photostabilization strategies required for reliable long-term imaging. Quantitative benchmarking protocols are introduced to evaluate signal-to-noise ratio, photobleaching kinetics, and spectral crosstalk, providing objective criteria for defining optimal operating conditions and instrument performance limits. Finally, we integrate these experimental procedures with an end-to-end single-molecule data-analysis workflow encompassing channel registration, automated and manual trajectory selection, FRET calculation, and kinetic analysis using hidden Markov modeling. The utility of the platform is demonstrated through smFRET measurements of conformational dynamics in a model nucleic acid system. Together, this work provides a reproducible and accessible methodology for implementing prism-based TIRF microscopy as a robust quantitative platform for single-molecule fluorescence studies across a wide range of biomolecular systems.

Biosensors doi: 10.3390/bios16060330

Authors: Sumin Hong Chul-Sung Park Kyung Wook Been Seunghun Kang Jaewoo Hong Jung-whan Kim Junho K. Hur

One of the major obstacles in early cancer detection in dogs is the limited sensitivity in detecting circulating tumor DNAs (ctDNAs) with low abundances. Standard next-generation sequencing (NGS) without error correction typically achieves detection limits around ~1% mutant allele frequency (MAF). We sought to improve the detection sensitivity using a sequential CRISPR-EspCas9 enrichment strategy in which iterative in vitro cleavage (IVC) was combined with PCR amplification to selectively deplete wild-type DNA and enrich rare tumor mutations. Applying the strategy to genomic DNA and cell-free DNA mimics from canine mammary gland tumor cell lines demonstrated that IVC enrichment enabled the detection of cancer-associated PIK3CA H1047R mutations that were undetectable by conventional Sanger sequencing. To evaluate detection sensitivity, we characterized enrichment using synthetic templates for PIK3CA H1047R and other cancer-related mutations, BRAF V596E, and KRAS G12C. We observed that three iterations of sequential IVC achieved ~160, ~15, and ~2.2-fold enrichment for PIK3CA H1047R, BRAF V596E, and KRAS G12C, respectively. Under the present synthetic-template conditions, the analytical LOD reached 0.001% MAF for PIK3CA and 0.01% MAF for BRAF, whereas KRAS showed only modest enrichment and remained practically limited under the current guide design. Together, the results show that the CRISPR-EspCas9 IVC strategy enables selective enrichment of low-frequency single-nucleotide mutant alleles. We anticipate that the finding could be utilized to develop a highly sensitive veterinary liquid biopsy application with further optimization and validation using canine plasma cfDNA.

Biosensors doi: 10.3390/bios16060329

Authors: Tong Zhang Xinyu Chen Qiangqiang Wang Shuyue Xing Dan Wu

Mycotoxins are toxic secondary metabolites produced by filamentous fungi, which extensively contaminate agricultural products such as grains and nuts. Common mycotoxins, including aflatoxin B1, ochratoxin A, and deoxynivalenol, can induce liver cancer, kidney damage, neural tube defects, and immune suppression, necessitating highly sensitive detection methods to ensure food safety. Conventional techniques are limited by complex procedures and insufficient sensitivity. Electrochemiluminescence (ECL), owing to its high sensitivity, low background signal, and rapid response, has emerged as a promising strategy for mycotoxin analysis. In this context, metal–organic frameworks (MOFs), with their high surface area and tunable structures, have been widely employed in ECL sensors to improve sensing performance. This review summarizes the construction strategies of MOF-based ECL sensors, the diverse functional roles of MOFs in ECL sensing, the associated sensing mechanisms, and the applications of these sensors for the detection of mycotoxins in food. Current challenges, including material stability, sensor reproducibility, and practical applicability, are discussed, and future directions are outlined. Particular emphasis is placed on the development of stable MOF materials, their integration into portable and intelligent ECL sensing platforms, and the establishment of standardized and scalable production methods to enable practical food safety monitoring.

Biosensors doi: 10.3390/bios16060328

Authors: Nan Xiang Chun-Yang Zhang

Biosensors are analytical devices that integrate a biological or synthetic recognition element (e [...]

Biosensors doi: 10.3390/bios16060327

Authors: Xinyue Li Xiaopeng Han Qing Han Xuan He Yixin Huang Aimei Liu

Nanobiosensors, with their unique physicochemical properties, are transformative tools for diagnosing and monitoring neurodegenerative diseases and mental disorders. This article systematically reviews the latest progress of nanomaterial systems and integrated sensing modalities in neurological disease diagnosis. First, we clarify the multiple functional roles of nanomaterials in biosensors, including signal amplification, interface optimization, and spatial positioning, and compare the applicable scenarios of various sensing principles based on different nanomaterials. Second, we evaluate the design and integration strategies of molecular recognition elements (antibodies, nucleic acid aptamers, molecularly imprinted polymers, and CRISPR-Cas systems) and discuss their synergistic integration mechanisms for improving detection performance. In terms of detection targets, we focus on three applications: high-sensitivity quantification of established protein biomarkers, real-time monitoring of dynamic neurochemicals (dopamine, serotonin, glutamate), and emerging liquid biopsy targets such as exosomal cargo and circulating microRNAs. Finally, to address the core challenges of biofouling, sensitivity–selectivity trade-offs, and multiplex detection in complex matrices, we propose three breakthrough directions for next-generation diagnostics: deep integration of multimodal and multiplexing platforms, closed-loop chemical brain–computer interfaces (cBCIs), and AI-driven predictive diagnostic models, collectively enabling a transition from passive detection to active sensing and intervention for precise, rapid, and non-invasive neurological disease management.

Biosensors doi: 10.3390/bios16060326

Authors: Abdullah Zulaikha Fatima Abdollah Abadian Carlos Guzmán Sánchez Mejorada Miguel Jesús Torres Ruiz Rolando Quintero Téllez

Electrocardiogram (ECG) analysis is a cornerstone non-invasive diagnostic technique for detecting cardiac arrhythmias, which remain a leading cause of mortality worldwide. While recent advances in deep learning have significantly improved automated arrhythmia classification, the current literature lacks systematic, fair comparisons of fundamental neural architectures under unified experimental conditions, and very few studies provide model interpretability. This study addresses these gaps by first providing a rigorous comparative analysis of three representative architectures—Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Residual Network (ResNet)—on the MIT-BIH Arrhythmia Database under identical preprocessing, training, and evaluation protocols. We then propose an efficient Fine-Tuned CNN (FT-CNN) optimized for ECG signal characteristics through adaptive kernel sizing for P-QRS-T morphological extraction, multi-faceted regularization including L2, dropout, and batch normalization, cosine annealing learning rate, and a custom loss function combining weighted categorical cross-entropy with focal loss with gamma equal to 2.0 to address severe class imbalance. The FT-CNN achieves an accuracy of 98.51%, outperforming fourteen benchmark models, including standard CNN with an accuracy of 97.20%, ResNet with 96.88%, LSTM with 96.50%, GRU with 96.30%, and traditional classifiers. Comprehensive ablation studies confirm an improvement of 6.17% over the baseline. Class-wise analysis reveals excellent performance for normal beats with an F1-score of 0.99, ventricular ectopic beats with 0.95, and unknown beats with 0.98, while supraventricular ectopic beats with an F1-score of 0.79 and fusion beats with 0.70 remain challenging. Unlike most prior works, we integrate Grad-CAM and Integrated Gradients for explainability, quantitatively evaluating attribution faithfulness, sanity checks, and noise robustness.

Biosensors doi: 10.3390/bios16060325

Authors: Zhengxin Zhou Muci Wu Rui Zhang Wangting Zhou Jiaojiao Zhou Jingren He

Food safety problems caused by pesticide residues, heavy metals, foodborne pathogens, mycotoxins and other hazards seriously threaten public health. Traditional detection methods have the limitations of cumbersome operation, high cost and poor stability, which make it difficult to meet the needs of rapid and sensitive detection on site. As a new material, nanozymes have the advantages of high stability, low cost and high catalytic activity, showing great application potential in food safety analysis. Among them, gold–platinum (AuPt) bimetallic nanozymes have attracted much attention due to their synergistic catalytic effect, good biocompatibility and modifiability. In this paper, the synthesis methods of AuPt bimetallic nanozymes were systematically reviewed, including chemical reduction, sol–gel, microemulsion, electrochemical deposition, and so on. The control effect of AuPt bimetallic nanozymes on catalytic activity was discussed from the aspects of composition, morphology, structure, external environment and composites with other nanomaterials. The research progress of AuPt bimetallic nanozymes in the detection of pesticide and veterinary drug residues, heavy metal ions, mycotoxins, foodborne pathogens, food additives and food freshness was introduced. Finally, the challenges and future development of AuPt bimetallic nanozymes in food safety analysis were prospected, aiming to provide theoretical reference and design ideas for the construction of a high-performance food safety rapid detection platform.

Biosensors doi: 10.3390/bios16060324

Authors: Po-Chung Chen Po-Chuan Hsieh

Celiac patients require strict avoidance of gliadin, the primary immunotoxic component of gluten, making sensitive detection essential for food safety. A label-free and reagentless capacitive aptasensor for gliadin detection was developed using an aminated polyethylene terephthalate (PET) thin film as both an insulating layer and functionalization platform. The PET surface was modified via ethylenediamine-mediated aminolysis, enabling covalent immobilization of 5′-NH2-modified gliadin aptamers through glutaraldehyde crosslinking. Under optimized conditions (23 µm initial PET thickness and 10 µM aptamer), the sensor showed a linear response from 10 to 500 µg/mL gliadin (R2 = 0.9792), with a detection limit of 6.0 µg/mL, equivalent to 12 ppm gluten, which is well below the regulatory threshold of 20 ppm for gluten-free labeling. The aptasensor showed excellent correlation with commercial ELISA for 20 gluten-containing soy sauce samples (R2 = 0.926) and spike recoveries of 91.7–105.7% in two gluten-free products. Efficient regeneration was achieved with 25 mM arginine (pH 9.0), retaining >80% activity after six cycles. This simple, low-cost, and reusable platform relies solely on a single PET thin film as consumable in a custom-built system with lab-friendly aminolysis conditions. It substantially lowers barriers to functionalized insulating layer fabrication, the primary challenge in capacitive aptasensor development, providing a promising method for on-site gliadin monitoring in gluten-free food safety applications.

Biosensors doi: 10.3390/bios16060323

Authors: Siyu Zhang Jingsong Liu Shoujiang Wu

ADHD is typically assessed through reports from parents, teachers, or clinicians, but these reports may not fully capture how motor behavior is organized at the signal level. This cross-sectional study examined whether X-axis acceleration features derived from a wearable device could provide preliminary evidence of ADHD symptom-related motor-pattern differences in children and adolescents. Primary school children aged 6–13 years wore an Apple Watch Series 7, and X-axis accelerometer signals were used to extract features reflecting waveform distribution, zero-crossing rate, micro-motion, and local movement fragmentation. ADHD symptoms and broader emotional and behavioral difficulties were assessed using the SNAP-IV and SDQ. The results showed that the X-axis zero-crossing rate was the most robust feature differentiating the High ADHD and Low ADHD groups across the T-task and F-task recordings. X-axis zero-crossing rate reached statistical significance (p = 0.029), indicating more frequent short-interval switching between positive and negative acceleration directions in children with higher ADHD symptom levels. This finding suggests that directional switching or local movement fragmentation in X-axis acceleration may be a sensitive movement characteristic associated with ADHD symptoms. In addition, X-axis skewness showed a consistent directional tendency, with higher values in the High ADHD group; this effect was marginal in the T-task recording (p = 0.065), suggesting a possible tendency toward waveform asymmetry or distributional imbalance during longer movement recording. Overall, these findings provide preliminary evidence that ADHD symptom-related motor differences may be reflected in the organization of X-axis acceleration signals, particularly in directional switching indexed by zero-crossing rate and, to a lesser extent, waveform asymmetry indexed by skewness. Given the cross-sectional design, symptom-based grouping, modest sample size, and incomplete recording-context control, these results should be interpreted cautiously and require confirmation in larger, diagnostically characterized samples with standardized wearable-recording protocols.

Biosensors doi: 10.3390/bios16060322

Authors: Haosong Du Yunxin Wang Ruochong Zhang Malini Olivo Renzhe Bi

Ultraviolet (UV) light is emerging as an important tool for biosensing, biomedical signal readout, and dose monitoring because of its strong and selective interactions with nucleic acids, proteins, and other biological components. This review summarizes recent progress in UV sensing-guided biomedical systems, with emphasis on three interconnected directions: label-free and surface-weighted imaging, wearable and embedded UV dosimetry, and sensor-assisted therapeutic guidance. Representative examples include ultraviolet photoacoustic microscopy (UV-PAM) for label-free nuclear imaging, microscopy with ultraviolet surface excitation (MUSE) for rapid slide-free histology-like readout, epidermal and flexible UV dosimeters for skin-level exposure quantification, and UV therapeutic platforms that are increasingly supported by sensing, dosimetry, and feedback for safer dose delivery. Across these applications, we emphasize the shared biosensing principles of signal generation, optical or acoustic transduction, quantitative readout, calibration, and feedback-informed decision support. We also discuss the role of artificial intelligence in virtual staining, image enhancement, domain correction, dose prediction, and decision support. The review concludes with key translational challenges in standardization, uncertainty quantification, multimodal integration, and feedback-driven system design. Overall, this sensing-centered perspective helps define the role of UV technologies more clearly within biosensors-oriented biomedical engineering.

Biosensors doi: 10.3390/bios16060320

Authors: Yea-Jin Park Dae-Hyun Jung

Herbal medicines represent a significant global market, yet food safety remains threatened by counterfeit products morphologically resembling authentic samples. Models trained on limited datasets are prone to shortcut learning, relying on superficial features rather than intrinsic morphological characteristics. This study identified size-based shortcut learning as a critical factor degrading the classification of Ziziphus jujuba Mill. var. spinosa and its counterfeit Ziziphus mauritiana Lam., and demonstrated that focal loss alone can effectively mitigate this issue. Models trained on the internal dataset were evaluated on an external dataset acquired with the Herb-X. On the internal test set, all configurations achieved high classification accuracies (≥98%), thereby obscuring meaningful differences in external generalization. However, consistent performance degradation was observed on the external dataset. The cross-entropy model trained on background-removed data dropped to 82.08 ± 10.97%, while size-normalized models recovered to 84.17 ± 10.15% (upsizing) and 88.94 ± 6.76% (downsizing), confirming that suppressing size shortcuts improves external generalization. The focal loss model, without any preprocessing, achieved 90.88 ± 2.71%, reducing the internal–external generalization gap from 16.18 to 8.11 percentage points. Grad-CAM++ and loss analyses confirmed that the focal loss model attended to intrinsic morphological features rather than object size. This study provides a practical, preprocessing-free approach for reliable herbal-medicine authentication in field conditions.

Biosensors doi: 10.3390/bios16060321

Authors: Qingfeng Zheng Zhun Zhuang Hua Lei Jianhan Lin Hua Yang Ruibin Hu

The contamination by foodborne pathogens posed a significant health threat and huge economic burden. Traditional detection methods were limited by cumbersome and time-consuming procedures, low automation, and reliance on expensive instrumentation, making them inadequate for on-site detection. This paper presented a centrifugal microfluidic chip that integrated sample pretreatment, nucleic acid extraction, amplification reaction, and signal detection. The chip featured an innovative design that combined a bursting valve with the siphon channel and employed a dual-channel configuration for splitting and directing the flow of different reagents, thereby overcoming the instability issue of unintended pre-activation or interruption that often occurred in the cascade design of multilevel siphon channels. Moreover, by synergistically combining with immunomagnetic separation as well as multi-enzyme isothermal rapid amplification, a portable, easy, rapid, high-sensitivity, and low-cost point-of-care testing (POCT) system for foodborne pathogens was developed. Under optimized conditions, the system enabled detection of Salmonella in spiked milk samples at 10 CFU/mL in 1 h. The recoveries ranged from 83.22% to 127.60%, with relative standard deviations of ≤13.7%, indicating that this system had great potential for rapid and high-sensitivity detection of foodborne pathogens in resource-limited settings.

Biosensors doi: 10.3390/bios16060318

Authors: William García-Rodríguez Karla Echeverría-Altamar José A. Lasalde-Ramirez Pedro J. Resto-Irizarry

This study investigates the fabrication of laser-scribed graphene (LSG) electrodes on polyimide substrates using a CO2 laser cutter for label-free L-Histidine detection in artificial sweat. Two-level full factorial and central composite designs were employed to optimize critical manufacturing parameters, including laser speed, power, and electrode width. Electrochemical characterization using cyclic voltammetry with K3Fe[CN]6 demonstrated superior LSG electrode performance compared to standard glassy carbon electrodes, exhibiting a 702 ± 62% higher oxidation current peak at 0.56 mM K3Fe[CN]6 in 0.1 M KCl. We successfully demonstrated the label-free electrochemical detection of L-Histidine in artificial sweat using these LSG electrodes. The results show a linear relationship (R2 = 0.987) between current peak and L-Histidine concentration within the 8.3 mM to 50 mM range, demonstrating high sensitivity towards L-Histidine. These findings highlight the potential of this optimized LSG electrode fabrication approach for developing high-performance, user-friendly, and disposable wearable biosensors for real-time and non-invasive health monitoring applications in sweat analysis.

Biosensors doi: 10.3390/bios16060319

Authors: Alexander N. Vaneev Petr V. Gorelkin Natalia L. Klyachko Alexander S. Erofeev

This review examines recent advances in multifunctional probes that integrate optical and electrochemical channels for in vitro/in vivo studies. Integration of electrodes with optical fibers provides a powerful platform for localized light delivery and simultaneous electrochemical detection of cellular metabolites both within and at the surface of single living cells. These hybrid devices bridge optical stimulation methods, including optogenetics, and electrochemical monitoring of the cellular response within the same experimental preparation. The review systematically categorizes distinct probe architectures: optical nanoendoscopes for intracellular measurements, probes with a shared opto-electrochemical channel, devices where optical and electrochemical channels are physically separated, and probes engineered for neural interfaces and scanning probe microscopy. For each category, fabrication approaches, surface modification strategies, and representative biological applications are discussed. Particular attention is given to the fundamental tension between optical transparency and electrical conductivity in shared-channel designs, to the mechanical requirements imposed by neural tissue on implantable probes, and to the spatial resolution limits of current scanning probe platforms. The review concludes with a critical assessment of current limitations and future directions, including higher spatial resolution, simultaneous multiplexed analyte detection and broader translation of these technologies toward in vivo experimental models.

Biosensors doi: 10.3390/bios16060317

Authors: Jieru Yang Junyao Li Xiao Han Zewen Wei Gang Wang Ting Zou

Sweat contains abundant ions, offering a rich source of physiological information for non-invasive health monitoring. Wearable sweat sensors have become a promising technology due to advances in electrochemical devices, sensing materials and structural design. The current monitoring platforms primarily employ two fundamental sensing modalities to convert sweat chemical information into detectable numerical signals: electrochemical (potentiometric, voltammetric, transistor-based) and optical (colorimetric) transduction mechanisms. The demand for more comprehensive physiological and biochemical data in clinical diagnosis and daily health monitoring is driving sensors towards multi-ion detection. Building on these modalities, researchers have optimized hardware and software algorithms based on the characteristics of different ions, thereby promoting the transition of wearable devices from the laboratory to practical applications. Here, we summarize recent progress in wearable sweat ion sensors, focusing on their mechanisms, advantages, and limitations. Finally, current challenges and future prospects of wearable sweat ion sensors for research applications, clinical use, and market demands are discussed.

Biosensors doi: 10.3390/bios16060315

Authors: Yuyan Xia Yurong She Xingguo Tian Huadong Zeng

The authentication of fish maw is of considerable importance for preventing product substitution and protecting market confidence in high-value aquatic foods. This study developed a rapid and nondestructive authentication strategy by combining hyperspectral imaging (HSI) with wavelength selection and a Kolmogorov–Arnold Network (KAN) to discriminate 10 commercially representative fish maw varieties. Hyperspectral datasets were collected in the visible and near-infrared (VNIR, 400–1000 nm) and short-wave infrared (SWIR, 900–1700 nm) regions. To improve spectral quality and model robustness, four preprocessing methods (SG, SG−MeanNor, SG−DT, and SG−SNV) were evaluated, followed by the construction of PLS-DA, SVM, MLP, CNN, and KAN models. Feature wavelengths were subsequently selected separately from the VNIR and SWIR spectra using CARS, iVISSA, and SPA to establish reduced-variable authentication models. The results showed that SG-DT achieved the best overall preprocessing effect, confirming its ability to reduce spectral noise and baseline variation. In addition, SWIR-based models consistently outperformed VNIR-based models, suggesting that compositional information captured in the SWIR region played an important role in fish maw authentication. Among all tested models, the SWIR@SG-DT-SPA-KAN model exhibited the best performance, achieving 98.67% accuracy, 98.75% precision, 98.67% recall, and 98.64% F1-score using only 16 SPA-selected wavelengths from the SG-DT-preprocessed SWIR spectra. This study demonstrates that HSI coupled with feature wavelength and KAN modeling can provide an accurate and efficient tool for fish maw authentication. More importantly, the reduced-wavelength model offers practical potential for developing fast and cost-effective multispectral systems for authenticity screening in the aquatic food market.

Biosensors doi: 10.3390/bios16060316

Authors: Zixuan Wang Xiaoyu Zhang Kexun Tang Liming Wu Yuxiang Huang Ning Zhang Bei Wang Xiaolong Wang Yi Ruan Qiang Lin

Magnetic sensors offer a physically grounded and non-invasive approach to probing biological processes that remain inaccessible to optical, electrochemical, and radio-frequency techniques in complex agricultural environments. In recent years, advances in both classical and quantum magnetic sensors have enabled the detection of bioelectromagnetic signals across plants, soils, animals, and aquatic systems, spanning spatial scales from ionic currents to organ-level electrophysiology and population-level dynamics, positioning magnetometry as an emerging modality within the broader biosensor landscape. This review surveys the evolution of magnetic sensing technologies for agricultural and animal systems, from robust classical sensors used in navigation and soil mapping to quantum-enabled platforms, including Optically Pumped Magnetometers (OPMs) and Nitrogen-Vacancy (NV) centers, capable of resolving pT to fT biomagnetic signals. We synthesize the characteristic amplitudes, frequency ranges, and physiological origins of agriculturally relevant magnetic signals, and critically assess how techniques originally developed for medical magnetoencephalography, magnetocardiography, and low-field magnetic resonance imaging (LF-MRI) are being translated into field-deployable agricultural applications. Beyond sensing hardware, we highlight the essential role of artificial intelligence in extracting weak biological signals from dominant environmental noise, enabling synthetic gradiometry, low-field image reconstruction, and scalable interpretation in unshielded settings. Finally, we discuss how the integration of magnetic biosensing with digital twins supports predictive, multiscale monitoring of plant, animal, and ecosystem health. Together, these developments position magnetometry as an enabling technology for next-generation biosensors in precision and sustainable agriculture.

Biosensors doi: 10.3390/bios16060314

Authors: Dehui Bi Yiran Cheng Xinyang Sun Yuancong Xu

Vitamin D3 is an essential fat-soluble vitamin for the human body. Its metabolite, 25-hydroxyvitamin D3 (25(OH)D3), serves as the primary biomarker to assess vitamin D levels. The monitoring of 25(OH)D3 concentration is crucial for human health assessment. While traditional detection methods offer high sensitivity and accuracy, they are operationally complex and costly. This review systematically summarizes the most recent progress in 25(OH)D3 detection technologies. Special attention is given to the recognition modes of 25(OH)D3 by antibodies, nucleic acids, and molecularly imprinted recognition elements. Subsequently, the design strategies of diverse types of biosensors, including fluorescent, colorimetric, and electrochemical biosensors, are analyzed. Moreover, the development of portable devices, smartphone software, and flexible wearable devices for detection applications is also examined. Biosensing detection platforms are compared from the perspectives of target recognition, signal conversion, signal output, and application scenarios. Additionally, the potential of biosensor detection platforms in clinical diagnosis, health management, and community health surveillance is further investigated. Finally, the future trends of intelligent, portable, accurate, and home-use 25(OH)D3 detection systems are delineated. This review offers a comprehensive reference for researchers developing next-generation 25(OH)D3 diagnostic sensors and provides insights for the early prevention and treatment of vitamin D deficiency-related diseases.

Biosensors doi: 10.3390/bios16060313

Authors: Suling Yang Xiaxin Chang Lin Liu

Protein biomarkers can be used for monitoring the occurrence and development of diseases. Accurate, sensitive, and low-cost methods for protein detection can facilitate therapeutic intervention, improve clinical outcome, and reduce economic pressure for patients. Molecularly imprinted polymers (MIPs) have been considered as a type of biomimetic materials for developing biosensing technologies due to their advantages of high stability, low preparation cost, and good reusability over classical biometric recognition elements such as antibodies and aptamers. Electrochemical biosensors have become the most promising technology in sensing applications in view of their high sensitivity, fast response speed, cost-effectiveness, good stability, and ease of miniaturization. Efforts have been made to develop various electrochemical biosensors for protein detection with MIPs as recognition elements. This article provides an overview of the progress in molecular imprinting methods for the design and application of electrochemical protein biosensors. The strategies for imprinting and removing templates and preparing MIPs-modified sensing electrodes are comprehensively discussed. Finally, the challenges and future perspectives of protein-imprinted electrodes are addressed. This work will contribute to the development of innovative analytical devices based on MIPs for monitoring and managing various diseases by determining protein biomarkers.

Biosensors doi: 10.3390/bios16060312

Authors: Mónica D. Garza-Villegas Itza E. Luna-Cruz Azael A. Cavazos-Jaramillo Juan M. Mora-Hernández Reyes Tamez-Guerra Cristina Rodríguez-Padilla Juan M. Alcocer-González

Acute respiratory diseases caused by viral pathogens such as Influenza A continue to represent a major global health challenge, emphasizing the need for rapid, sensitive, and accessible diagnostic tools. In this work, a carbon screen-printed electrode (SPCE)-based electrochemical immunosensor for the detection of an Influenza A (H1) antigen is reported, incorporating a comparative electrochemical evaluation of four electrode materials. Fe3O4 nanoparticles, Fe3O4@C nanoparticles, graphene quantum dots (GQDs), and gold nanoparticles (AuNPs) were systematically assessed by cyclic voltammetry to evaluate their electrocatalytic performance. The highest electrochemical response was selected for biosensor construction. The immunosensor was fabricated by immobilizing antibodies on a modified SPCE and characterized using differential pulse voltammetry (DPV). A concentration-dependent response was observed for H1 antigen concentrations ranging from 0 to 300 ng/mL, with a minimum detectable concentration (MDC) of 1 ng/mL and limit of detection (LOD) of 176 ng/mL and 45 ng/mL for serum and nasopharyngeal swabs, respectively. The biosensor performance was specifically evaluated in complex biological fluids, demonstrating reproducible performance and moderate selectivity against non-target influenza subtypes. Overall, this study highlights the critical role of electrode material selection in determining electrochemical immunosensor performance and supports the potential of SPCE-based platforms for the screening of an Influenza A (H1) antigen in point-of-care-oriented applications.

Biosensors doi: 10.3390/bios16060311

Authors: Xiqiong Mu Yaqin Dong Ling Jin Tiantian Zhu

This study compares the performance of novel fluorescent nanobiosensors and liquid chromatography–mass spectrometry (LC-MS) for detecting organophosphorus pesticide residues in Angelica sinensis. Key parameters such as recovery rate and relative standard deviation (RSD) were evaluated to identify a preferable detection method. The results show that the fluorescent nanobiosensor offers superior sensitivity, whereas LC-MS excels in accuracy and quantitative analysis. For phoxim, the GQDs@GSH sensor achieved an LOD of 0.075 μmol·L−1, with recoveries of 97.65–100.62% (RSD < 3.35%). For glyphosate, the PDOA/Cu2+ sensor achieved an LOD of 1.8 nmol·L−1, and the AgNCs sensor achieved an LOD of 21 nmol·L−1, with recoveries of 91.30–105.34% (RSD < 3.35%). The originality of this work is threefold and does not claim de novo synthesis of unknown materials: (i) it is the first validation of three existing fluorescent nanosensors (GQDs@GSH, PDOA/Cu2+, and AgNCs) specifically for organophosphorus pesticide detection in the complex medicinal plant matrix Angelica sinensis; (ii) it provides the first systematic comparison of these nanosensors against the pharmacopeia-standard LC-MS method in this matrix, delineating their complementary advantages; (iii) it is the first assessment of their phytotoxicity in Angelica sinensis seedlings, demonstrating biocompatibility and potential for in planta imaging.

Biosensors doi: 10.3390/bios16060310

Authors: Ali Narin Rukiye Uzun Arslan Damla Kırkıl

Early and reliable identification of cardiac disorders from Phonocardiogram (PCG) signals acquired from wearable biosensors is critical to support clinical decision making and reduce subjectivity in auscultation-based assessments. This study proposes a multi-stage hybrid feature selection-classification approach to increase diagnostic accuracy without requiring computationally expensive deep learning (DL) architectures. First, the most statistically discriminative features were identified using mRMR, ReliefF, and Kruskal–Wallis filtering methods. Particle swarm optimization (PSO) and ant colony optimization (ACO) were then applied to optimize the solution space. Finally, the selected feature subsets were tested with k-nearest neighbor (k-NN), support vector machines (SVMs), and Bagged Tree (BT) classifiers. Experimental results show that the proposed method significantly increases the model robustness and generalizability. In particular, the Kruskal–Wallis+k-NN and ReliefF+k-NN combinations achieved competitive performance compared to many DL-based approaches in the literature, with 99.80% accuracy and 99.50% F1-score. Furthermore, hybrid models augmented with PSO and ACO also achieved 99.60% accuracy. The findings demonstrate that well-designed feature selection strategies offer high accuracy and enhanced clinical applicability while using only a small set of handcrafted features and conventional classifiers. Therefore, the proposed framework is a strong candidate for smart stethoscope-based early screening solutions.

Biosensors doi: 10.3390/bios16060309

Authors: Truong-Tu Truong Yumi Kaku Gonzalo Bustos-Quevedo Sara ElGenk Ehsan Mahmodi Arjmand Gustav Grether Jan Lüddecke Judith Schlanderer Stefan Wagner Theresa Katschmareck Eva Dazert Nikolas von Bubnoff Irina Nazarenko Germán Matías Hansen Sabrina Kartmann Tobias Hutzenlaub Nils Paust Peter Juelg

Background: The growing demand for versatile laboratory automation is exemplified in the context of liquid biopsy, where multi-analyte approaches are increasingly recognised for their potential to enhance diagnostic sensitivity in oncology. However, current practice often necessitates the use of dedicated instruments and workflows for the extraction of each analyte, posing financial and logistical barriers for automated multi-analyte liquid biopsy. Methods: Here, we present Robotic Centrifugal Microfluidics (RoCM), an all-in-one platform that combines the versatility of centrifugal microfluidics and operational flexibility of robotic liquid handling. This combination enables the automation of complex micro- and macrofluidic protocols, realised through the use of (1). exchangeable microfluidic cartridges and (2). programmable robotic operations such as in-rotation liquid supply, magnetic bead manipulation, or microfluidic valving. In-rotation robotic liquid manipulation maintains fluid control under centrifugal forces and reduces the cartridge footprint associated with pre-loaded liquid reservoirs. Platform applicability was demonstrated using two exemplary liquid biopsy workflows: the extraction of cell-free DNA (cfDNA) from blood plasma using RoCM-cfDNA slices and the extraction of extracellular vesicles (EVs) from blood plasma using RoCM-EV slices. Results: In a pilot study with patient samples from different cancer entities, the RoCM-cfDNA slices yielded comparable variant allele frequencies to a commercial bead-based instrument, while the RoCM-EV slices achieved a recovery of a greater diversity of EV subpopulations than semi-automated size-exclusion chromatography. Conclusions: By simply exchanging cartridges, RoCM enables the extraction of diverse analytes within a single automated system. Its application can be extended to further analytes, such as circulating tumour cells (CTCs), or to applications beyond liquid biopsies, where versatile micro- and macrofluidic protocols benefit from implementation in a single automation instrument.

Biosensors doi: 10.3390/bios16060308

Authors: Dingshi Xu Xuehan He Yi Zhao Jiasi Wang Lei Chen

We present an optimized method for producing semiconducting polymer dots using a water–ethanol mixed antisolvent during nanoprecipitation. Compared to conventional Pdots made with pure water as the antisolvent, these newly produced Pdots exhibit simultaneously enhanced fluorescence efficiency and stability of particle size and emission spectra. These findings should be mainly attributed to an improved core–shell Pdots nanostructure formed by a sequential nanoprecipitation process. It offers Pdots a purer, more compact, and hydrophobic inner core, coated with a greater number of hydrophilic polyethylene glycol shells. This viewpoint is further reinforced by Förster energy-transfer efficiency in a fluorescence donor-acceptor Pdots system. The novelly prepared Pdots can better encapsulate small-molecular cargoes and more efficiently bioconjugate to targets. Consequently, it demonstrates improved specific immunofluorescence staining of microtubule structures in living cells.

Biosensors doi: 10.3390/bios16060307

Authors: Shuqin Li Xiu-Hong Wang

Diabetic chronic wounds, characterized by persistent inflammation and a complex microenvironment, pose a major challenge to global healthcare. Traditional dressings act merely as passive physical barriers, lacking the ability to sense biochemical fluctuations or respond to dynamic pathological changes. Therefore, developing smart platforms for in situ, continuous, and non-invasive monitoring is crucial for early warning and precision intervention. This review systematically explores recent advances in high-fidelity wound monitoring, focusing on the deep integration of “front-end interface engineering” and “back-end data analysis”. We first analyze the specific physicochemical and biochemical abnormalities of the diabetic wound microenvironment. Next, we discuss how advanced material designs, such as active fluid management, anti-biofouling zwitterionic networks, and nanozyme-based reactive oxygen species (ROS) scavenging, ensure the long-term stability of sensing interfaces against complex microenvironmental interference. Building on this hardware foundation, we summarize in situ sensing strategies and multiparameter decoupling techniques tailored for key biomarkers, including pH, temperature, glucose, ROS, and MMP-9. Furthermore, we highlight cutting-edge developments in signal digitization, emphasizing the pivotal role of portable devices and machine learning algorithms in extracting high-dimensional features and translating complex multimodal signals into objective clinical metrics. By outlining this comprehensive technological closed-loop, this review aims to provide a systematic theoretical framework for the development and clinical translation of next-generation smart wound monitoring platforms.

Biosensors doi: 10.3390/bios16060306

Authors: Michal Pecik Erik Vavrinsky Diana Vitazkova Helena Kosnacova Juraj Nevrela Erik Foltan

Advances in wearable device and sensor technologies progressively shift respiratory monitoring from the clinical setting to real-world conditions. This rapidly developing field allows for more accurate diagnostics. However, reliable monitoring during dynamic activities remains challenging due to artifacts caused by movement, postural changes, electrode drift, and variability in breathing patterns. Therefore, this review focuses on wearable methodologies capable of determining respiratory rate and potentially tidal volume during strenuous physical activities. Direct sensing approaches, including chest and abdominal belts, bioimpedance principles, and inertial sensing units, are complemented by indirect methods derived from ECG and PPG signals. Hybrid systems, which are also discussed, represent a very promising approach. Special attention is paid to signal processing, machine learning, and multimodal sensor fusion algorithms that improve robustness and reliability. By systematically analyzing hardware and software combinations, validation protocols, and current limitations, this article identifies emerging trends in adaptive respiratory monitoring. This review aims to guide the development of next-generation wearable systems.

Biosensors doi: 10.3390/bios16060305

Authors: Mingda Zhao Yuxing Zhang Yibo Wang Hui Liu Mingxiao Li Yang Zhao Lingqian Zhang Chengjun Huang

Brain-on-a-chip (BOC) refers to a miniaturized in vitro platform that integrates living neuronal networks on a micro-engineered chip, enabling the simulation of brain functions, neural activities and physiological responses. BOC technology is an advanced evolution of microphysiological systems (MPS) and Lab-on-a-Chip platforms, providing novel paradigms for in vitro modeling and exploring early-stage biocomputing by interfacing living neural networks with engineered electronics. Microelectrode arrays (MEAs) serve as the critical physical interface for bidirectional communication in these systems. In this review, we systematically examine the technological landscape and engineering requirements of MEAs tailored for BOC applications, evaluating them across electrical characteristics, structural properties, and biocompatibility. Two primary classes of current MEA technologies, including planar arrays for 2D neural cultures and 3D flexible arrays for brain organoids, are discussed in detail. We highlight the transition from passive planar electrodes to high-density active CMOS and TFT-based arrays, and detail how 3D flexible MEAs utilize endogenous integration and exogenous wrapping strategies to overcome tissue-mechanics mismatches. Furthermore, the integration of MEAs with microfluidics, optoelectronics, and electrochemical sensors to enable multimodal monitoring is explored. With the advantages of the various MEAs, the application of MEAs for BOC, particularly in biological computing and network plasticity research, is discussed. Finally, future technological developments in scalability bottlenecks, chronic stability, and the incorporation of artificial intelligence for MEAs of BOC are prospected.

Biosensors doi: 10.3390/bios16060304

Authors: Binbin Gao Guohui Li Milica Balaban Vesna Antic Muhammad Zeeshan Tahir Li Gao

Two-dimensional-material-based FET biosensors have gained attention for being label-free and having ultra-sensitive detection capability. The high carrier mobility and large surface-to-volume ratio of 2D materials enable low detection limits under buffer conditions; however, practical detection still faces many challenges. Current reviews have largely summarized materials, functionalization routes, or target classes separately, but a clearer framework linking interface design, device architecture, and practical sensing performance is still needed. In this review, we examine how interfacial engineering and device architecture govern signal transduction and sensing behavior in 2D material FET biosensors. We also analyze the major barriers to real-sample detection, including Debye screening, nonspecific adsorption, and signal drift, together with commonly used mitigation strategies. On this basis, an “interface–device–performance” framework is discussed as a conceptual approach for understanding the relationship between molecular recognition, electrical response, and sensing performance. This review mainly focuses on the key challenges of 2D material FET biosensors in practical medical applications, discusses the differences between material and application perspectives, and examines the major factors limiting clinical translation.

Biosensors doi: 10.3390/bios16060302

Authors: Giorgia Polidori Andrea Visani Gianluca Giavaresi Mauro Serpelloni Gregorio Marchiori

Sensors are an established driver of diagnostics and prevention in the medical field, including orthopaedics. Today, the subclass of ion-selective sensors (ISSs) is on the leading edge due to its advantages, enabled by technological advancements in manufacturing, such as miniaturization, precision, accuracy, specificity, a wide measuring scale, ease of use, flexible operating conditions, and measuring speed. While ISSs’ impact on environmental and health fields is already the subject of investigation, it still needs to be analysed specifically in orthopaedics, which is the aim of this Review. A PubMed and Scopus search was performed using the keywords “ion”, “sensor”, “electrodes”, “selective”, “musculoskeletal”, “implant”, “joint replacement”, and “orthopaedic”; after systematic screening, 44 studies were included in the synthesis. First, studies were classified based on the target ion. Only a few papers treated applications specifically in orthopaedics, confirming that ISSs are still largely an unexplored frontier here. However, all of the studies targeted ions with a role also in musculoskeletal pathophysiology, thus relative ISSs could have a potential impact on orthopaedic diagnosis and treatment. Then, when described by the papers, ISSs’ technological solutions were systematically evaluated. Finally, the main ISSs development targets for reaching orthopaedic clinical application were highlighted, including biocompatibility (e.g., implantability), long-term stability, calibration, and validation. Overcoming these challenges will enable ISSs to progress from laboratory prototypes to clinically viable tools, supporting the advancement of next-generation sensorised prostheses, fixation devices, and surgical instruments, and paving the way for predictive and personalised orthopaedic medicine.

Biosensors doi: 10.3390/bios16060303

Authors: Yu Chen Lina Dong Hengyan Tian Fei Yang Dengbang Jiang Minglong Yuan

Magnetic polymeric microspheres are pivotal solid-phase carriers in chemiluminescence enzyme immunoassays (CLEIA). However, their practical clinical application is frequently hindered by non-specific adsorption, irreversible aggregation, and the intrinsic susceptibility of exposed outermost Fe3O4 nanoparticles to oxidation. To overcome these critical bottlenecks, we rationally engineered highly original monodisperse P(St-MMA)@Fe3O4@P(St-MMA) sandwich-structured microspheres. The bespoke amphiphilic outer shell acts as an impenetrable shield against hydration and oxidation, while maintaining a topologically size-matched mesoporous network (average pore size of 13.11 nm) for optimal antibody anchoring. Strikingly, this architecture ensures exceptional long-term colloidal stability, completely preventing macroscopic agglomeration for over six months in buffer solutions. When evaluated in a carcinoembryonic antigen (CEA), CLEIA, our microspheres achieved an ultra-low limit of detection (LOD) of 0.055 ng·mL−1 and high analytical recovery (93.37–108.25%). In a head-to-head comparison with industry-standard commercial magnetic beads, the engineered microspheres delivered stronger chemiluminescent signals and lower background noise, demonstrating excellent intra-assay (CV < 4.37%) and inter-assay (CV < 10%) precision. This work establishes a scalable, highly stable materials platform that effectively resolves the persistent oxidation limitations, holding immense practical importance for next-generation ultrasensitive clinical in vitro diagnostics.

Biosensors doi: 10.3390/bios16050301

Authors: Jingjing Liu Yuxuan Cao Jiale Kuang Zhongren Wei Boyu Liu Xianghao Wu Bolin Shi Lei Zhao Dongfu Xu Xinyu Wang Kui Zhong

Mastication and swallowing are complex physiological processes involving the coordinated activity of multiple tissues in the oral cavity, facial region, and laryngeal system. Some detection methods suffer from limitations such as insufficient information acquisition and inadequate temporal feature analysis. To address these issues, this study proposes a conceptual method for analyzing the state of masticatory and swallowing movements. It integrates maxillofacial electromyographic (EMG) signals with laryngeal movement signals. The goal is to preliminarily explore state analysis of masticatory and swallowing movements over time. A designed gain-adjustable conditioning circuit processes and acquires these signals: maxillofacial EMG signals from EMG electrodes and laryngeal movement signals from flexible PVDF piezoelectric sensors. These two signal streams complement each other’s missing information, enabling comprehensive detection of the state of masticatory and swallowing movements. To address time-point labeling in mastication and swallowing, a sliding-window-based dispersion calculation method was employed to extract characteristic signal nodes, which were then accurately associated with their corresponding physiological motion states. We combined temporal features such as the zero point, onset of fluctuations, characteristic peaks, and baseline recovery from electromyographic (EMG) signals and laryngeal movement signals. This allowed us to establish a correspondence between key time points in the mastication and swallowing processes. The coefficient of determination (R2) for the pressure–voltage linear fit of the PVDF flexible piezoelectric sensor was 0.99446. The pressure resolution was approximately 0.08 kPa. Response times were no more than 15 ms for the EMG channel and no more than 10 ms for the PVDF pressure channel. These results indicate that this method is feasible for extracting oral movement time parameters in healthy subjects.

Biosensors doi: 10.3390/bios16050300

Authors: Marco Cujilema Ramon Villarino David Girbau Antonio Lazaro

Although compression therapy is widely used to improve wound healing, selecting the appropriate pressure remains a challenge in clinical practice. This work proposes an intelligent patch integrated into a bandage that allows for the simultaneous monitoring of the applied pressure and wound condition using Near-Field Communication (NFC). The proposed patch integrates a force-sensitive resistive sensor to measure pressure and a capacitive sensor to detect wound exudate through capacitance variations. Capacitance is obtained by analyzing the delay in the stepwise response of the sensor, while resistance is measured from the voltage drop across a resistive divider, which is read by a microcontroller’s analog-to-digital converter. The system is powered wirelessly through NFC energy harvesting, triggered by a mobile device that acts as a reader. The NFC module can be moved away after measurement to improve patient comfort or remain integrated into the dressing for periodic monitoring. Experimental results demonstrate pressure measurements up to 140 mmHg and exudate detection up to 200 μL, confirming the feasibility of battery-free NFC smart bandages for therapeutic monitoring based on wound compression.

Biosensors doi: 10.3390/bios16050299

Authors: Mosebudi Rambevha Ridge Chavalala Philani Mashazi

Ascorbic acid (AA) or vitamin C is an important biomolecule that plays a crucial role in biological and physiological systems. Deficiency and/or excess of AA in the body can lead to severe diseases such as scurvy and gastrointestinal complications. Therefore, it is crucial to monitor the levels of AA in the body and supplements. Polyvinylpyrrolidone nickel oxide nanoparticles (PVP-NiONPs) are prepared and evaluated for their potential as nanozymes with peroxidase-like activity. o-Phenylenediamine (OPD) was used as a chromogen in the presence of hydrogen peroxide. The oxidized OPD was produced by ROS from PVP-NiONPs and H2O2. This was monitored using UV-vis spectra and by colour changes using the naked eye. AA reduced the oxidized OPD during its sensing. The UV-vis signal was linear for AA concentrations ranging from 40 µM to 400 μM. The limit of detection (LOD) for AA was calculated to be 0.11 μM using 3σ and the limit of quantification (LOQ) was 0.36 μM using 10σ indicating a very high sensitivity. The colorimetric sensor showed good reproducibility and a recovery rate between 92.3% and 102.6%, indicating high accuracy and reliability. The findings of this work confirmed that PVP-NiONPs possess enzyme-like activity and are a promising alternative for the quantitative, on-site detection of ascorbic acid.

Biosensors doi: 10.3390/bios16050298

Authors: Zala Štukovnik Nik Perko Urban Bren

This study presents a rapid, eukaryotic impedimetric biosensor that applies the yeast Saccharomyces cerevisiae as a robust, cost-effective biorecognition element for monitoring the acute toxicity of two representative pharmaceuticals, naproxen and isoniazid, in aquatic systems. The biosensor utilizes a previously developed three-electrode system made from type 316 stainless steel. Yeast cells seeded onto these electrodes serve as the biosensing element. By monitoring changes in electrical impedance, the system quantifies the cellular stress induced by pharmaceutical exposure. Electrochemical Impedance Spectroscopy (EIS) revealed a concentration-dependent decrease in both resistance and capacitance, attributed to cell death and subsequent desorption from the working electrode surface. These findings were validated through optical density at 600 nm (OD600) growth curve analysis and methylene blue viability staining, which confirmed metabolic inhibition and membrane damage. Results indicate a linear response for naproxen within the 2.5 mM to 20 mM range, with a LOD of 0.509 mM, and for isoniazid within the 10 mM to 100 mM range, with a LOD of 0.684 mM. Naproxen demonstrated a more pronounced cytotoxic effect, with cell viability dropping to 41.08% at 10 mM compared to 68.79% for isoniazid. While conventional analytical methods focus on chemical quantification, this proof-of-concept biosensor provides a rapid toxic/non-toxic signal, offering a biologically relevant tool for real-time monitoring of industrial waste streams and acute environmental contamination.

Biosensors doi: 10.3390/bios16050297

Authors: Changjie Bao Honglin Zhang Lin Jiang Tianhui Liu Wei Liu Qi Qi Xuejiao Ren Hongxun Fu Meiyan Sun

Synthetic biology is reshaping in vitro diagnostics (IVD) by enabling programmable and modular biosensing elements that can be integrated into point-of-care testing (POCT) platforms. Compared with conventional assays that depend on fixed chemistries and centralized instrumentation, synthetic biology-based systems offer adaptable molecular recognition, tunable signal processing, and flexible readout formats for decentralized diagnostics. In this review, we present synthetic biology-enabled IVD as programmable biosensing platforms organized into four functional layers: molecular recognition, signal transduction and amplification, output generation, and system integration. We discuss four major enabling modules, including cell-free protein synthesis (CFPS) systems, aptamer and riboswitch sensors, CRISPR-Cas diagnostic platforms, and microfluidic integration technologies. We summarize representative clinical applications from 2021 to 2025 in infectious disease detection, cancer biomarker analysis, and drug metabolism/toxicity screening. In addition, we examine practical considerations beyond analytical sensitivity, including matrix tolerance, workflow complexity, manufacturability, quantitative capability, and regulatory readiness. Finally, we highlight future directions for programmable diagnostics, including AI-assisted biosensor design, multimodal readouts, interoperable platform architectures, and real-world clinical validation.

Biosensors doi: 10.3390/bios16050296

Authors: Yu Tang Luhua Meng Yun Liu Xiang Ma

Continuous intraocular pressure (IOP) monitoring is crucial for glaucoma management. Currently, traditional static IOP measurements often fail to detect circadian fluctuations, leading to a clinical dilemma where “normal IOP” is observed despite persistent visual field deterioration. This study presents a wireless, passive localized surface plasmon resonance (LSPR) sensing platform integrated into flexible silicone hydrogel contact lenses. Gold nanoparticles (AuNPs), synthesized via the sodium citrate reduction method, were incorporated into the lens periphery using a “swelling-induced nano-doping” technique to transduce IOP-induced corneal strain into detectable spectral shifts. Ex vivo porcine eye investigations established a physical mapping model, confirming significant LSPR peak wavelength response trends in correlation with IOP variations (10–50 mmHg) and corneal curvature changes. Subsequent 21-day in vivo rabbit studies demonstrated excellent ocular surface biocompatibility; quantitative histopathological analysis (HE, PAS, and Ki67 staining) revealed no significant adverse alterations in corneal endothelial cell density or conjunctival goblet cell function compared to control groups (p > 0.05). Furthermore, the platform maintained high structural integrity and anterior segment tolerance under transient high-IOP conditions. While currently a proof-of-concept, these results indicate that the LSPR-active hybrid system effectively captures dynamic IOP fluctuation patterns as an optical response to acute interventions, providing a foundational engineering path for next-generation, battery-free wearable diagnostics in personalized glaucoma care without the need for built-in electronics.

Biosensors doi: 10.3390/bios16050295

Authors: He Wang Hong Men Yan Shi

Traditional taste evaluation relies heavily on manual sensory analysis, which is highly subjective and inefficient with poor cross-individual generalization, limiting its application in industrial flavor detection. To achieve accurate cross-subject taste recognition, this paper proposes an electroencephalogram (EEG) classification method based on a meta-learning wavelet graph convolutional neural network (ML-WGCNet) under sweet- and bitter-taste stimuli. Sucrose (sweetness) and quinine (bitterness) were used as stimulation sources, each prepared at six concentration gradients, including a water control. EEG signals were detected from 20 subjects. First, the Morlet wavelet transform was applied to decompose the EEG signals in the time–frequency domain, extracting the maximum and average energy values from five frequency bands as core features. A graph structure was then constructed using electrodes as nodes and Pearson correlation coefficients between electrodes as edge weights. A lightweight graph convolutional neural network (GCN) is employed to model spatial correlations among brain regions. Finally, by integrating a meta-learning framework and adopting leave-one-subject-out cross-validation, the model can rapidly adapt to new subjects. The experimental results show that the proposed method achieves average accuracies of 76.03% and 77.01% in cross-subject classification of sweet and bitter tastes, respectively. The corresponding precision values are 79.94% and 79.53%, the recall values are 75.77% and 78.51%, and the F1-scores are 78.24% and 78.08%, respectively, demonstrating that the proposed model significantly outperforms existing mainstream EEG classification methods.

Biosensors doi: 10.3390/bios16050294

Authors: Xinyi Sheng Guo-Jun Zhang Jie Zhou

Exosomes are a kind of nanoscale extracellular vesicle secreted by almost all cell types and considered promising biomarkers for disease diagnosis since they could carry abundant proteins, nucleic acids, and lipids that reflect parental cell states. However, conventional exosome detection methods suffer from several limitations including insufficient specificity, low throughput, high costs, and inadequate sensitivity for clinical applications. By contrast, field-effect transistor (FET) biosensors are a promising alternative by enabling label-free, real-time, and ultrasensitive detection of exosomes through direct transduction of biorecognition events into electrical signals. This review first introduces the fundamental principles and device structure of FET biosensors, as well as exosome isolation strategies. The recent advances in exosome analysis using FET-based biosensors are then presented, which are categorized into two primary strategies: (1) direct detection of intact exosomes based on surface markers, including tetraspanin proteins (CD9, CD63, CD81, etc.) and disease-specific biomarkers, and (2) detection of exosomal contents including microRNA and protein biomarkers following exosome lysis. Finally, we discuss current challenges of FET-based exosome detection and provide perspectives on future developments.

Biosensors doi: 10.3390/bios16050293

Authors: Ting Liu Haogang Guo Mengmeng Yu Jiawei Peng Liwen Guan Shuying Xie Xian Hao Yifei Yang

Schistosomiasis, caused by Schistosoma species, is notoriously difficult to accurately diagnose with conventional methods. In this study, we present an innovative biosensor that integrates CRISPR–Cas12a technology with nucleic acid aptamers for the highly sensitive detection of Schistosoma japonicum eggs. The biosensor leverages a Y-shaped DNA structure (Y-DNA) that incorporates an aptamer specific to S. japonicum eggs, along with an activator DNA and a segment for immobilization on magnetic nanomaterials. Upon target recognition, the Y-DNA releases the activator, which triggers the collateral cleavage activity of Cas12a, enabling the direct detection of eggs. This system demonstrates remarkable sensitivity, being capable of detecting individual eggs in infected rabbit serum and feces. Moreover, it effectively distinguishes the eggs of S. japonicum from those of other parasitic species. The simplicity, high sensitivity, and rapid detection of our biosensor offer significant potential for improving the diagnosis of schistosomiasis, providing a novel, reliable tool for early detection in clinical settings.

Biosensors doi: 10.3390/bios16050292

Authors: Longchao Qi Kaibo Guo Xianlong Ning Yiming Huang Xun Lu

Noble metal nanostructures based on localized surface plasmon resonance (LSPR) can induce metal-enhanced fluorescence (MEF) and surface-enhanced Raman scattering (SERS), significantly improving trace detection sensitivity for biomedical and chemical analysis. While self-assembly of noble metal nanoparticles offers simplicity and low equipment dependence, achieving large-area, uniform, and controllable nanostructures remains challenging. In this study, angle-dependent dip coating (ADDC) technology was employed to achieve efficient, controllable self-assembly of silver nanoparticles (AgNPs) on glass slides, establishing a fabrication process for MEF/SERS dual-functional substrates. A stable AgNPs-anhydrous ethanol suspension was prepared and extracted from an inclined substrate reservoir using a microfluidic syringe pump, enabling large-area uniform nanostructure assembly. Systematic investigation revealed that substrate inclination angle provides better morphology and fluorescence enhancement control than withdrawal flow rate. The silver nanostructured surface fabricated under a withdrawal flow rate of 16 mL/h and a substrate inclination angle of 30° exhibited a Cy3 detection limit as low as 10−1 nM, with an enhancement factor ranging from 19.14 to 28.66, as well as an R6G SERS detection limit of 10−10 M with an enhancement factor of 4.07 × 108. This study confirms that ADDC technology enables simple, efficient, large-area uniform AgNPs self-assembly for superior dual-function enhancement substrates, offering a cost-effective and efficient strategy for highly sensitive trace detection.

Biosensors doi: 10.3390/bios16050291

Authors: Dakota Brown Wendell Manuel Dan Luu Tri-Duc Luong Marienette Morales Vega Manh-Huong Phan

The low signal-to-noise ratio (SNR) of existing magnetic sensors limits the detection of magnetic nanoparticles (MNPs) in microfluidic biosensing. We present a novel microfluidic coil-based impedance detection system for quantifying magnetic particles, including Fe filings and citrate-coated Fe3O4 MNPs, with potential applications in magnetically guided biosensing. Unlike conventional approaches that directly measure the magnetic properties of dispersed particles, our method employs an external collector magnet to concentrate particles within a copper coil detector. The accumulated particles alter the coil’s electromagnetic response through changes in the sample’s dielectric properties, producing an amplified impedance signal proportional to sample volume. We evaluated detection performance for 1–10 mg of ferromagnetic Fe filings and citrate-coated Fe3O4 MNPs across a broad frequency range. Results show a strong linear correlation between particle mass and impedance change, with SNR values from 25 dB to over 45 dB, demonstrating high sensitivity and precision. Coil sensitivity was further optimized by varying the number of turns (5, 10, and 15), enabling frequency-specific customization. This approach provides a scalable, low-cost platform adaptable to polymer-coated MNPs targeting biological analytes.

Biosensors doi: 10.3390/bios16050290

Authors: Xiao Hong Stella W. Pang

Understanding cell migration is essential not only for fundamental biology but also for the development of targeted disease therapies. Traditional in vitro cell migration assays typically rely on optical microscopy to capture cell movements and subsequent image-based tracking to quantify cell migration characteristics, which often involve substantial experimental workload and analytical complexity. Therefore, there is a need for an automated and streamlined approach to monitor and analyze cell movements. In this work, a microfabricated impedance sensor integrating electrode pairs and selectively protein-coated channels was developed for real-time monitoring of single-cell migration. The optimized electrode dimensions with 10 μm width and 10 μm gap enabled sensitive detection of impedance magnitude increase induced by individual cells. The impedance magnitude changes were correlated with the cell coverage area on electrodes, allowing continuous tracking of single-mouse osteoblast MC3T3 cell movement across the electrode pair. Distinct impedance responses of signal duration and magnitude were observed under different surface coatings, revealing the influence of microenvironmental chemistry on cell motility and adhesion. Furthermore, comparative impedance profiling of MC3T3 and nasopharyngeal epithelial NP460 cells demonstrated that MC3T3 cells produced larger changes in impedance real part and phase due to larger spreading area and larger number of focal adhesions, whereas NP460 cells showed shorter impedance signal change durations, consistent with faster cell migration. These electrical signatures collectively captured intrinsic differences in cell morphology, adhesion, and motility. The developed impedance sensor provides a label-free approach for single-cell migration characterization and can be potentially applied to cell identification.

Biosensors doi: 10.3390/bios16050289

Authors: Felix Aberer Daniel A. Hochfellner Petra M. Baumann Bernhard Höll Peter Beck Thomas R. Pieber Julia K. Mader

Background: Algorithm-based insulin dosing systems are increasingly used in hospitals and have shown the potential to efficiently and safely enable glycemic control. The goal of this study was to evaluate glycemic control using the ultralong-acting basal insulin degludec (IDeg) in combination with insulin aspart (IAsp) within an algorithm-driven electronic clinical decision support system (cDSS) in inpatients with type 2 diabetes (T2D). Methods: In this non-controlled single-arm pilot study, an electronic, algorithm-based cDSS was applied for the management of insulin treatment in an internal general ward. Thirty hospitalized patients with T2D (18 female, age 74.1 ± 10.9 years, HbA1c 72.4 ± 22.3 mmol/mol, BMI 28.6 ± 5.6 kg/m2, diabetes duration 13.2 ± 11.6 years, creatinine 1.5 ± 1.2 mg/dL, length of hospital stay 9.1 ± 4.0 days) were included in the study. Capillary blood glucose (BG) was evaluated four times daily using a point-of-care device integrated into the hospital information system. In addition, all participants received a blinded continuous glucose monitoring (CGM; Abbott Freestyle Libre Pro) system. The primary endpoint was defined as the percentage of BG measurements within the target range of 3.9–7.8 mmol/L. Results: Overall, 722 BG values and 17,242 CGM data points were available. Of those, 52.2% and 55.0% were in the specified target area (3.9–7.8 mmol/L), respectively. Mean BG prior to study start was 11.9 ± 4.4 mmol/L and improved to 7.5 ± 1.9 mmol/L and 7.4 ± 1.4 mmol/L after 6 and 10 days of treatment. BG < 3.9, <3.0 and <2.2 mmol/L was 1.25%, 0.28% and 0%, respectively. Adherence to the total daily insulin dose suggested by the cDSS was 94.2%, and 99.5% of all basal and 85.3% of all bolus insulin suggestions were accepted by the nurses in charge. Basal-bolus therapy using the cDSS covered 85% of the participants’ total hospital stay. Conclusions: Glycemic control using IDeg within an algorithm-driven cDSS could effectively and safely be achieved in the hospital and was highly accepted.

Biosensors doi: 10.3390/bios16050288

Authors: Nan Li Xingliang Liu Boyuan Shi Chunhui Song Teng Zhang Xin Yan Yingying Li Xinyi Li Jun Ma

The abuse of γ-hydroxybutyric acid (GHB) and its precursors, γ-butyrolactone (GBL) and 1,4-butanediol (1,4-BD), has increased in recent years, with these substances frequently being illicitly added to beverages. GHB is colorless and odorless and exhibits anesthetic and hypnotic psychoactive effects, which are often exploited in drug-facilitated sexual assault, posing a significant public safety concern. Chromatography–tandem mass spectrometry is a conventional analytical approach for narcotic drug determination due to its high sensitivity and accuracy; however, its large instrumentation footprint and high operational cost limit its suitability for on-site rapid screening. In response to the growing demand for field-deployable analytical tools, rapid detection technologies for GHB have progressively evolved. This review summarizes and compares the advantages and limitations of current rapid detection methods for GHB and discusses their potential future developmental trends, with the aim of providing a reference for researchers and relevant authorities.

Biosensors doi: 10.3390/bios16050287

Authors: Anupamaa Sivasubramanian Shankara Narayanan Gymama Slaughter

Chronic kidney disease (CKD) is a progressive and irreversible disorder affecting over 850 million individuals globally and is associated with significant morbidity, mortality, and healthcare burden. Conventional diagnostic approaches rely on intermittent laboratory measurements, including serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin, which provide limited temporal resolution and fail to capture dynamic physiological changes. Recent advances in wearable biosensing technologies offer new opportunities for continuous, non-invasive monitoring of biochemical and physiological markers relevant to renal function. This review provides a comprehensive analysis of wearable biosensors for CKD monitoring, focusing on sensing mechanisms (electrochemical, optical, and field-effect transistor), biofluid interfaces (sweat, interstitial fluid, and saliva), and materials engineering strategies enabling flexible, high-performance devices. Emphasis is placed on biofluid transport dynamics, analytical performance across sampling matrices, and system-level integration with wireless communication and digital health platforms. Key challenges limiting clinical translation, including biofouling, enzymatic instability, and variability in biofluid composition, are examined—alongside emerging solutions such as antifouling interfaces, synthetic recognition elements, and multimodal sensing architectures. Finally, regulatory pathways and the role of artificial intelligence in digital nephrology are discussed. This review highlights the potential of wearable biosensors to transform CKD management through continuous monitoring, early detection, and personalized therapeutic intervention.

Biosensors doi: 10.3390/bios16050286

Authors: Albina Abdossova Toheeb Olalekan Oladejo Sabira Seipetdenova Marzhan Nurlankyzy Aigerim Omirzakova Aidana Bissen Aliya Bekmurzayeva Carlo Molardi Cevat Erisken Wilfried Blanc Daniele Tosi

A semi-distributed interferometer is a low-reflectivity device with refractive index sensing capability, exploiting the random reflectivity of a nanoparticle-doped fiber to form a weak distributed cavity. In this work, we extend this concept to an extrinsic semi-distributed interferometer (ESDI), using an overlay made of polydimethylsiloxane (PDMS) around the fiber tip; this structure can then be surface-modified using a thin metallic film or a nanoparticle coating. We report gold-sputtered and gold-nanoparticle-coated ESDI structures for refractive index sensing capability, with the latter achieving superior performances with an average sensitivity of 62.8 dB/RIU (refractive index units) with resolution of 3.9 × 10−5 RIU over the range of 1.34790–1.35981. We also report a possible biological application using a biofunctionalized version of this probe for the detection of VEGF (vascular endothelial growth factor); the gold-sputtered probe achieves the highest sensitivity, 0.0565 dB for each 10× concentration increase, with 355 fM detection limit.

Biosensors doi: 10.3390/bios16050285

Authors: Zekun Zhang Heng Liu Ruide Li Huiyuan Zhu Fan Li Shujun Ni Aojun Liu Yao Zhai

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

Biosensors doi: 10.3390/bios16050284

Authors: Christian Martinez Samuel Groysman Madison Ngo Yixian Wang

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

Biosensors doi: 10.3390/bios16050283

Authors: Abdullateef Gbolahan Olayiwola Albina Abdossova Daniele Tosi Gorka Orive Zhe Liu Cevat Erisken

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

Biosensors doi: 10.3390/bios16050282

Authors: Jiwon Yun Inyoung Kim Wonil Nam

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

Biosensors doi: 10.3390/bios16050281

Authors: Vasiliki E. Georgakopoulou Vassiliki C. Pitiriga

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