Biosensors-Basel最新文献

Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor's performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5-8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM^-1 cm^-2. Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology.

The rapid and sensitive detection of Salmonella typhimurium in food matrices is crucial for ensuring food safety. This study presents the development of an ultrasensitive electrochemical biosensor using surface-modified bacterial cellulose (BC) integrated with polypyrrole (Ppy) and reduced graphene oxide (RGO), further functionalized with immobilized S. typhimurium-specific phage particles. The BC substrate, with its ultra-fibrous and porous structure, was modified through in situ oxidative polymerization of Ppy and RGO, resulting in a highly conductive and flexible biointerface. The immobilization of phages onto this composite was facilitated by electrostatic interactions between the polycationic Ppy and the negatively charged phage capsid heads, optimizing phage orientation and enhancing bacterial capture efficiency. Morphological and chemical characterization confirmed the successful fabrication and phage immobilization. The biosensor demonstrated a detection limit of 1 CFU/mL for S. typhimurium in phosphate-buffered saline (PBS), with a linear detection range spanning 10⁰ to 10⁷ CFU/mL. In real samples, the sensor achieved detection limits of 5 CFU/mL in milk and 3 CFU/mL in chicken, with a linear detection range spanning 10⁰ to 10⁶ CFU/mL, maintaining high accuracy and reproducibility. The biosensor also effectively discriminated between live and dead bacterial cells, demonstrating its potential in real-world food safety applications. The biosensor performed excellently over a wide pH range (4-10) and remained stable for up to six weeks. Overall, the developed BC/Ppy/RGO-phage biosensor offers a promising tool for the rapid, sensitive, and selective detection of S. typhimurium, with robust performance across different food matrices.

Lung cancer is a major threat to human health and a leading cause of death. Accurate localization of tumors in vivo is crucial for subsequent treatment. In recent years, fluorescent imaging technology has become a focal point in tumor diagnosis and treatment due to its high sensitivity, strong selectivity, non-invasiveness, and multifunctionality. Molecular probes-based fluorescent imaging not only enables real-time in vivo imaging through fluorescence signals but also integrates therapeutic functions, drug screening, and efficacy monitoring to facilitate comprehensive diagnosis and treatment. Among them, near-infrared (NIR) fluorescence imaging is particularly prominent due to its improved in vivo imaging effect. This trend toward multifunctionality is a significant aspect of the future advancement of fluorescent imaging technology. In the past years, great progress has been made in the field of NIR fluorescence imaging for lung cancer management, as well as the emergence of new problems and challenges. This paper generally summarizes the application of NIR fluorescence imaging technology in these areas in the past five years, including the design, detection principles, and clinical applications, with the aim of advancing more efficient NIR fluorescence imaging technologies to enhance the accuracy of tumor diagnosis and treatment.

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

Owing to their unique physicochemical properties, MXenes have emerged as promising materials for biosensing applications. This review paper comprehensively explores the recent advancements in MXene-based biosensors for health and environmental applications. This review begins with an introduction to MXenes and biosensors, outlining various types of biosensors including electrochemical, enzymatic, optical, and fluorescent-based systems. The synthesis methods and characteristics of MXenes are thoroughly discussed, highlighting the importance of these processes in tailoring MXenes for specific biosensing applications. Particular attention is given to the development of electrochemical MXene-based biosensors, which have shown remarkable sensitivity and selectivity in detecting various analytes. This review then delves into enzymatic MXene-based biosensors, exploring how the integration of MXenes with enzymes enhances sensor performance and expands the range of detectable biomarkers. Optical biosensors based on MXenes are examined, focusing on their mechanisms and applications in both healthcare and environmental monitoring. The potential of fluorescent-based MXene biosensors is also investigated, showcasing their utility in imaging and sensing applications. In addition, MXene-based potential wearable biosensors have been discussed along with the role of MXenes in volatile organic compound (VOC) detection for environmental applications. Finally, this paper concludes with a critical analysis of the current state of MXene-based biosensors and provides insights into future perspectives and challenges in this rapidly evolving field.

Biosensors are attractive tools for detecting molecules and small particles, as they can produce rapid, sensitive, and specific signals [...].

Synthetic microwave focusing methods have been widely adopted in qualitative medical imaging to detect and localize anomalies based on their electromagnetic scattering signatures. This paper discusses the principles, challenges, and limitations of synthetic microwave-focusing techniques in medical applications. It is shown that the various focusing techniques, including time reversal, confocal imaging, and delay-and-sum, are all based on the scalar solution of the electromagnetic scattering problem, assuming the imaged object, i.e., the tissue or object, is linear, reciprocal, and time-invariant. They all aim to generate a qualitative image, revealing any strong scatterer within the imaged domain. The differences among these techniques lie only in the assumptions made to derive the solution and create an image of the relevant tissue or object. To get a fast solution using limited computational resources, those methods assume the tissue is homogeneous and non-dispersive, and thus, a simplified far-field Green's function is used. Some focusing methods compensate for dispersive effects and attenuation in lossy tissues. Other approaches replace the simplified Green's function with more representative functions. While these focusing techniques offer benefits like speed and low computational requirements, they face significant ongoing challenges in real-life applications due to their oversimplified linear solutions to the complex problem of non-linear medical microwave imaging. This paper discusses these challenges and potential solutions.

This review examines recent advancements in electrochemical immunosensors for the detection of organophosphate pesticides, focusing on strategies to enhance sensitivity and selectivity. The widespread use of these pesticides has necessitated the development of rapid, accurate, and field-deployable detection methods. We discuss the fundamental principles of electrochemical immunosensors and explore innovative approaches to improve their performance. These include the utilization of nanomaterials such as metal nanoparticles, carbon nanotubes, and graphene for signal amplification; enzyme-based amplification strategies; and the design of three-dimensional electrode architectures. The integration of these sensors into microfluidic and lab-on-a-chip devices has enabled miniaturization and automation, while screen-printed and disposable electrodes have facilitated on-site testing. We analyze the challenges faced in real sample analysis, including matrix effects and the stability of biological recognition elements. Emerging trends such as the application of artificial intelligence for data interpretation and the development of aptamer-based sensors are highlighted. The review also considers the potential for commercialization and the hurdles that must be overcome for widespread adoption. Future research directions are identified, including the development of multi-analyte detection platforms and the integration of sensors with emerging technologies like the Internet of Things. This comprehensive overview provides insights into the current state of the field and outlines promising avenues for future development in organophosphate pesticide detection.

Sweat is considered the most promising candidate to replace conventional blood samples for noninvasive sensing. There are many tools and optical and electrochemical methods that can be used for detecting sweat biomarkers. Electrochemical methods are known for their simplicity and cost-effectiveness. However, they need to be optimized in terms of selectivity and catalytic activity. Therefore, electrode modifiers such as nanostructures and metal-organic frameworks (MOFs) or combinations of them were examined for boosting the performance of the electrochemical sensors. The MOF structures can be prepared by hydrothermal/solvothermal, sonochemical, microwave synthesis, mechanochemical, and electrochemical methods. Additionally, MOF nanostructures can be prepared by controlling the synthesis conditions or mixing bulk MOFs with nanoparticles (NPs). In this review, we spotlight the previously examined MOF-based nanostructures as well as promising ones for the electrochemical determination of sweat biomarkers. The presence of NPs strongly improves the electrical conductivity of MOF structures, which are known for their poor conductivity. Specifically, Cu-MOF and Co-MOF nanostructures were used for detecting sweat biomarkers with the lowest detection limits. Different electrochemical methods, such as amperometric, voltammetric, and photoelectrochemical, were used for monitoring the signal of sweat biomarkers. Overall, these materials are brilliant electrode modifiers for the determination of sweat biomarkers.

In this paper, we present a sensitive and highly reproducible fluorescence immunosensor for detecting PSA in human serum. A unique feature of this study is that it uses creatively designed paddle screw-type devices and their custom-made rotating system for PSA immunoassay. The paddle screw devices were designed to maximize the surface-to-volume ratio over which the immunoassay reaction could occur to improve detection sensitivity. This paddle screw-based immunoassay offers an accessible and efficient method with a short analysis time of less than 30 min. Active rotation of the paddle screw plays a crucial role in fast and accurate analysis of PSA. Additionally, a paddle screw-based immunoassay and subsequent fluorescence detection using a custom prototype fluorescence detection system were compared to a typical well plate-based immunoassay system. Results of PSA detection in human serum showed that the detection sensitivity through the paddle screw-based analysis improved about five times compared to that with a well plate-based analysis.