Biosensors-Basel最新文献

MicroRNAs, pivotal regulators of gene expression and physiology, serve as reliable biomarkers for early cancer diagnosis and therapy. As one of the earliest discovered miRNAs in the human genome, miRNA-21 provides critical information for early cancer diagnosis, drug therapy, and prognosis. In this work, we harness CRISPR as a bridge to integrate target-induced self-priming hairpin isothermal amplification (SIAM) with terminal transferase (TdT) polymerization labeling, constructing a facile, straightforward electrochemical biosensor for sensitive miRNA-21 detection. Unlike conventional single-strand template-based exponential amplification (EXPAR), the SIAM hairpin undergoes target triggered intramolecular conformational change, initiating extension and strand displacement reactions that suppress nonspecific dimer formation and lower background current. Notably, the assay requires only a single probe, enabling unidirectional signal amplification while nonspecific reactions caused by system complexity. The generated SIAM products activate the Cas12a/crRNA complex to trans-cleave PO₄^3- modified single-stranded DNAs (ssDNAs); the resulting 3' hydroxyl ssDNAs are subsequently labeled by TdT, with the assistance of SA-HRP catalyzing hydrogen peroxide, achieving robust signal amplification. Under optimized conditions, the cathodic current exhibits a logarithmic relationship with miRNA concentrations from 20 fM to 5.0 × 10⁸ fM, with a detection limit of 9.2 fM. The biosensor successfully quantified miRNA-21 in commercial serum samples and biological lysates, demonstrating its potential for cancer diagnostics and therapy.

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

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

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

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

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of R2=0.697 and R2=0.794 for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops.

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

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

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

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