Biosensors-Basel最新文献

Sulfamethazine (SMZ) is widely used in livestock production, and its residues can enter water and soil environments, posing potential risks to human health and ecosystems. This study focuses on environmental samples and constructs an AuNP-based colorimetric aptasensor using the SMZ1S aptamer for the rapid visual detection of SMZ. Under optimized conditions, the aptasensor showed a wide linear range from 0.05 to 0.4 µg/mL and a limit of detection of 0.039 µg/mL. Molecular dynamics simulations have demonstrated that the aptamer's binding to SMZ is stable, providing a theoretical basis for the high selectivity of the aptasensor. Spike-and-recovery experiments yielded recoveries of 87.3-105.5%, 88.6-102.8%, and 87.5-103.4% for SMZ in lake water, tap water, and soil samples, respectively, with relative standard deviations of 5.9-8.3%, 8.0-10.6%, and 4.8-9.6%, showing good agreement with high-performance liquid chromatography (HPLC) results (R² ≥ 0.981). Overall, the proposed aptasensor provides a simple and effective approach for rapid detection of SMZ in environmental samples.

The increasing impact of infectious, cardiovascular and neurodegenerative diseases has intensified the demand for early and decentralized diagnostics. Label-free electrochemical biosensors are promising candidates, offering high sensitivity, low reagent consumption and miniaturizable, low-cost architectures for point-of-care (PoC) testing. This review summarizes advances in immobilization strategies, recognition elements such as DNA, antibodies, aptamers, and molecularly imprinted polymers, as well as electrode platforms including glassy carbon, screen-printed, and 3D-printed systems, with an emphasis on DNA biosensors, multiplexed configurations, and applications to disease biomarkers. Beyond analytical performance, we critically examine the barriers that keep most devices at the proof-of-concept stage, including bioreceptor stability and immobilization, limited validation in real samples, reliance on conventional materials, challenges in scalable manufacturing, transport, and storage, and the absence of fully integrated PoC systems. Finally, we discuss significant advances in sensitivity, reproducibility, and application to real samples, but note that translation to real-world use and commercialization remains limited.

In on-site rapid detection, the electrochemical method boasts high sensitivity and rapid response capabilities, while the colorimetric method can provide intuitive visual readings suitable for on-site screening. Therefore, this study developed an innovative dual-mode electrochemical/colorimetric aptasensor for the accurate detection of malathion (MAL) in vegetables. The sensor combines magnetic Fe₃O₄@ZIF-8-DNA composites and CuZr-MOF-cDNA probes, enabling simultaneous detection of the target through electrochemical reactions and colorimetric changes. The introduction of CuZr-MOF not only enhances the sensor's conductivity but also significantly amplifies the electrochemical signal through its catalytic properties. The magnetic Fe₃O₄@ZIF-8-DNA composite facilitates solid-liquid separation under an external magnetic field. When the target MAL is present, the aptamer binds to the target, causing the CuZr-MOF-cDNA probes to release from the composite, altering the number of free probes in the supernatant and generating varying intensities of colorimetric signals. Meanwhile, the MAL captured in the precipitate by the aptamer is quantitatively detected through electrochemical methods. Experimental results demonstrate that as the target concentration increases, the colorimetric signal intensifies while the electrochemical signal weakens, showing a good linear relationship between the two. The aptasensor's limit of detection (LOD) for colorimetric and electrochemical modes was 1.57 × 10^-11 M and 4.76 × 10^-11 M, respectively, with recoveries ranging from 87.71% to 107.68% and relative standard deviations between 3.23% and 10.75%. This method exhibits high sensitivity, excellent selectivity, and strong reliability, providing a novel technique for the accurate quantification of MAL in vegetables, particularly suited for on-site rapid detection.

The ultrasensitive detection of microRNA-17 (miRNA-107) is required for clinical diagnosis. In this work, an aggregation-induced electrochemiluminescence (AIECL) sensor was developed for the quantification of miRNA-107, in which AIECL-active polymer dots (Pdots) were characterized by transmission electron microscopy, ultraviolet-visible spectroscopy, and cyclic voltammetry and used as ECL emitters. Black hole quencher-labeled hairpin DNA (HP-BHQ) was modified on the Pdot surfaces, resulting in the ECL signal of the Pdots being in the "off" state due to the resonant energy transfer (RET) between the BHQ and Pdots. In the presence of miRNA-107, HP-BHQ opened through RNA-DNA hybridization. Subsequently, the introduced duplex-specific nuclease (DSN) facilitated the cleavage of DNA in the RNA-DNA hybrid chain and led to the detachment of HP-BHQ from the electrode surface. The ECL signal of the Pdots recovered, i.e., to the "on" state. The variation in the ECL signal was related to the concentration of the target miRNA-107. As a result, the AIECL biosensor exhibited a wide linear response to miRNA-107 concentrations ranging from 1.0 fM to 10.0 pM, and a low detection limit of 0.82 fM. This work provides a novel platform for the sensitive analysis of miRNA.

Accurate identification and characterization of subcutaneous tumors are essential for improving breast tumor detection and treatment. This study introduces an innovative Tactile-Sensation Imaging System (TSIS) designed, implemented, and tested to detect and characterize subcutaneous inclusions simulating breast tumors. The system employs a multilayered polydimethylsiloxane (PDMS) optical waveguide that mimics the tactile structure of the human fingertip. By introducing light at a critical angle, the design enables continuous total internal reflection (TIR) within the flexible, transparent waveguide. When external pressure is applied, deformation of the contact area causes light scattering, which is recorded using a high-definition camera and processed as tactile images. Analysis of these images allows estimation of inclusion characteristics such as size, depth, and mechanical properties, including Young's modulus. Analytical modeling and numerical simulations validated the optical performance of the waveguide, while experimental evaluations using realistic tissue phantoms confirmed the system's ability to accurately detect and quantify embedded inclusions. The results demonstrated reliable estimations of inclusion dimensions, depths, and stiffness, verifying the system's sensitivity and precision. The TSIS offers a noninvasive, portable, and cost-efficient solution for quantitative breast tumor assessment, bridging the gap between manual palpation and advanced imaging, with future enhancements aimed at improving resolution and diagnostic accuracy.

Background: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into sport and exercise through wearable biosensing systems that enable continuous monitoring and data-driven training adaptation. However, their practical value for coaching depends on the validity of biosensor data, the robustness of analytical models, and the conditions under which these systems have been empirically evaluated. Methods: A structured narrative review was conducted using Scopus, PubMed, Web of Science, and Google Scholar (2010-2026), synthesising empirical and applied evidence on wearable biosensing, signal processing, and ML-based adaptive training systems. To enhance transparency, an evidence map of core empirical studies was constructed, summarising sensing modalities, cohort sizes, experimental settings (laboratory vs. field), model types, evaluation protocols, and key outcomes. Results: Evidence from field and laboratory studies indicates that wearable biosensors can reliably capture physiological (e.g., heart rate variability), biomechanical (e.g., inertial and electromyographic signals), and biochemical (e.g., sweat lactate and electrolytes) markers relevant to training load, fatigue, and recovery, provided that signal quality control and calibration procedures are applied. ML models trained on these data can support training adaptation and recovery estimation, with improved performance over traditional workload metrics in endurance, strength, and team-sport contexts when evaluated using athlete-wise or longitudinal validation schemes. Nevertheless, the evidence map also highlights recurring limitations, including sensitivity to motion artefacts, inter-session variability, distribution shift between laboratory and field settings, and overconfident predictions when contextual or psychosocial inputs are absent. Conclusions: Current empirical evidence supports the use of AI-driven biosensor systems as decision-support tools for monitoring and adaptive training, but not as autonomous coaching agents. Their effectiveness is bounded by sensor reliability, appropriate validation protocols, and human oversight. The most defensible model emerging from the evidence is human-AI collaboration, in which ML enhances precision and consistency in data interpretation, while coaches retain responsibility for contextual judgement, ethical decision-making, and athlete-centred care.

Lateral flow immunoassays (LFAs) are widely used for on-site testing; however, their use for the rapid detection of plant viruses in the field is often limited by inconvenient sample preparation. Here, we present a new sampling method and a simplified dipstick LFA format for the detection and monitoring of cowpea chlorotic mottle virus (CCMV) as a model plant pathogen. The assay employs a monoclonal mouse antibody for capture and a poly-clonal rabbit antibody conjugated to 80 nm gold nanoparticles for detection. Conventional sample and conjugate pads are omitted, allowing the test strips to be dipped directly into wells containing plant extract and antibody-gold conjugate. No plastic casing was required, which could lead to a reduction in waste. It was shown that CCMV concentrations as low as 3.5 µg/L or 350 pg per sample could be reliably detected in 15 min. Specificity tests confirmed that other plant viruses, cowpea mosaic virus (CPMV) and tobacco mosaic virus (TMV), did not produce false-positive results. In addition, we describe a new method for on-site sampling using a manual punch and a syringe equipped with a frit. This step combines grinding the sample, extraction, filtration, and reconstitution and mixing of the antibody-gold conjugate, enabling the analysis of punched leaf disks without laboratory equipment. When applied to CCMV-infected cowpea plants, the assay revealed systemic infection before visual symptoms became apparent. This work demonstrates that simplified LFAs combined with innovative sampling techniques can provide sensitive, specific, and rapid diagnostics for crop monitoring and support early intervention strategies in agriculture.

Cerium oxide (CeO₂) nanozymes, also known as nanoceria have emerged as a versatile class of catalytic nanomaterials capable of mimicking key redox enzymes, including oxidases and peroxidases. Their tunable Ce³⁺/Ce⁴⁺ redox cycling, high density of oxygen vacancies, and exceptional resistance to thermal, pH, and storage stress distinguish CeO₂ from conventional enzyme labels, such as horseradish peroxidase (HRP). In immunoassays, CeO₂ enables H₂O₂-free TMB (3,3',5,5'-tetramethylbenzidine) oxidation, generating strong chromogenic signals with minimal background. Although CeO₂ nanozymes have been explored in colorimetric, chemiluminescent, and photoactive immunoassays, their integration into lateral flow immunoassays (LFIAs) remains limited, with only a few hybrid CeO₂-containing systems reported to date. This mini-review highlights the limitations of conventional peroxidase-based formats and explains how CeO₂'s redox cycling (Ce³⁺/Ce⁴⁺) and oxygen-vacancy-driven catalysis deliver stable, reagent-free signal amplification. Emphasis is placed on the synthetic control of CeO₂, conjugation chemistry with antibodies, and integration into LFIA architectures. CeO₂ enables hydrogen-peroxide-free colorimetric detection with improved robustness and sensitivity, positioning it as a promising catalytic label for point-of-care testing. However, it may aggregate in high-ionic-strength buffers, and its synthesis cost increases for highly uniform, vacancy-engineered materials. Surface functionalization with polymers or dopants and optimized dispersion strategies can mitigate these issues, guiding future practical implementations.

The demand for rapid and highly sensitive sensing technologies is increasing across diverse fields, including precise disease diagnosis, early-stage screening, and real-time environmental monitoring. Field-effect transistor (FET)-based sensing platforms have shown tremendous potential for detecting target molecules at extremely low concentrations, owing to their ultrahigh sensitivity, label-free and amplification-free operation, and rapid response. In recent years, the rapid advancement of nucleic acid probe design and interfacial engineering has markedly accelerated the development of FET sensors, leading to the emergence of nucleic acid-based FET (NA-FET) biosensors. Beyond their fundamental role in nucleic acid detection, the integration of nucleic acid aptamers and framework nucleic acids has greatly expanded NA-FET biosensors' applicability to a wide range of analytes and multiplexed detection. At the same time, advances in semiconductor materials have endowed the NA-FET biosensor with highly efficient signal transduction and diverse device architectures, enabling successful proof-of-concept demonstrations for various clinically and environmentally relevant molecular biomarkers. Furthermore, the integration into portable, wearable, and implantable devices has laid a solid foundation for their future development into real-world applications. This review summarizes recent cutting-edge progress in NA-FET biosensors, highlights key design strategies and performance improvements, and discusses current challenges, future development directions, and their prospects for practical applications.

The precise and reliable detection of milk adulterants has garnered increased scientific interest owing to the rising incidence of food fraud. Recent years have witnessed substantial advancements in optical and electrochemical biosensors for the quick, sensitive, and on-site determination of adulterants. This review thoroughly emphasizes recent developments in electrochemical biosensors, encompassing amperometric, voltammetric, impedimetric, and photoelectrochemical sensors, alongside optical biosensors such as colorimetric, fluorometric, and plasmonic systems. Significant focus is directed towards determination of critical milk adulterants, including variations in pH, urea, formaldehyde (FA), melamine (MEL), nitrates (NO₃^-), nitrites (NO₂^-), and sulfites (SO₃^2-). The sensing mechanisms, functional nanomaterials, analytical efficacy, and sample-handling techniques of the described biosensors are critically examined. Moreover, key challenges regarding matrix interference, sensor stability, reproducibility, regulatory validation, and large-scalability are addressed. Ultimately, future directions towards economical, portable, wearable, and Internet of Things (IoT)-integrated biosensors for continuous dairy monitoring are discussed, highlighting the necessity for standardized validation protocols and next-generation technologies in food safety.