Development and optimization of a frequency mixing sensor for adjacent samples quantitative detection on a lateral flow assay.

Frequency-mixing technology has been widely used to precisely identify magnetic nanoparticles in applications of quantitative biomedical detection in recent years. Examples include immune adsorption, lateral flow assays (LFAs), and biomagnetic imaging. However, the signals of magnetic response generated by adjacent magnetic samples interfere with each other owing to the small spacing between them in applications involving multi-sample detection (such as the LFA and multiplexing detection). Such signal interference prevents the biosensor from obtaining characteristic peaks related to the concentration of adjacent biomarkers from the magnetic response signals. Mathematical and physical models of the structure of sensors based on frequency-mixing techniques were developed. The theoretical model was verified and its key parameters were optimized by using simulations. A new frequency-mixing magnetic sensor structure was then designed and developed based on the model, and the key technical problem of signal crosstalk between adjacent samples was structurally solved. Finally, standard cards with stable magnetic properties were used to evaluate the performance of the sensor, and strips of the gastrin-17 (G-17) LFA were used to evaluate its potential for use in clinical applications. The results show that the minimum spacing between samples required by the optimized sensor to accurately identify them was only about 4-5 mm, and the minimum detectable concentration of G-17 was 11 pg mL-1 . This is a significant reduction in the required spacing between samples for multiplexing detection. The optimized sensor also has the potential for use in multi-channel synchronous signal acquisition, and can be used to detect synchronous magnetic signals in vivo.

Immunochromatographic enhancement strategy for SARS-CoV-2 detection based on nanotechnology.

The global outbreak of coronavirus disease 2019 (COVID-19) has been catastrophic to both human health and social development. Therefore, developing highly reliable and sensitive point-of-care testing (POCT) for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a priority. Among all available POCTs, the lateral flow immunoassay (LFIA, also known as immunochromatography) has proved to be effective due to its accuracy, portability, convenience, and speed. In areas with a scarcity of laboratory resources and medical personnel, the LFIA provides an affordable option for the diagnosis of COVID-19. This review offers a comprehensive overview of methods for improving the sensitivity of SARS-CoV-2 detection using immunochromatography based on nanotechnology, sorted according to the different detection targets (antigens, antibodies, and nucleic acids). It also looks into the performance and properties of the various sensitivity enhancement strategies, before delving into the remaining challenges in COVID-19 diagnosis through LFIA. Ultimately, it seeks to provide helpful guidance in selecting an appropriate strategy for SARS-CoV-2 immunochromatographic detection based on nanotechnology.

Dual-signal readout paper-based wearable biosensor with a 3D origami structure for multiplexed analyte detection in sweat.

In this research, we design and implement a small, convenient, and noninvasive paper-based microfluidic sweat sensor that can simultaneously detect multiple key biomarkers in human sweat. The origami structure of the chip includes colorimetric and electrochemical sensing regions. Different colorimetric sensing regions are modified with specific chromogenic reagents to selectively identify glucose, lactate, uric acid, and magnesium ions in sweat, as well as the pH value. The regions of electrochemical sensing detect cortisol in sweat by molecular imprinting. The entire chip is composed of hydrophilically and hydrophobically treated filter paper, and 3D microfluidic channels are constructed by using folding paper. The thread-based channels formed after the hydrophilic and hydrophobic modifications are used to control the rate of sweat flow, which in turn can be used to control the sequence of reactions in the differently developing colored regions to ensure that signals of the best color can be captured simultaneously by the colorimetric sensing regions. Finally, the results of on-body experiments verify the reliability of the proposed sweat sensor and its potential for the noninvasive identification of a variety of sweat biomarkers.

Point-of-care biochemical assays using electrochemical technologies: approaches, applications, and opportunities.

Against the backdrop of hidden symptoms of diseases and limited medical resources of their investigation, in vitro diagnosis has become a popular mode of real-time healthcare monitoring. Electrochemical biosensors have considerable potential for use in wearable products since they can consistently monitor the physiological information of the patient. This review classifies and briefly compares commonly available electrochemical biosensors and the techniques of detection used. Following this, the authors focus on recent studies and applications of various types of sensors based on a variety of methods to detect common compounds and cancer biomarkers in humans. The primary gaps in research are discussed and strategies for improvement are proposed along the dimensions of hardware and software. The work here provides new guidelines for advanced research on and a wider scope of applications of electrochemical biosensors to in vitro diagnosis.

Rapid segmentation and sensitive analysis of CRP with paper-based microfluidic device using machine learning.

Microfluidic paper-based analytical devices (µPADs) have been widely used in point-of-care testing owing to their simple operation, low volume of the sample required, and the lack of the need for an external force. To obtain accurate semi-quantitative or quantitative results, µPADs need to respond to the challenges posed by differences in reaction conditions. In this paper, multi-layer µPADs are fabricated by the imprinting method for the colorimetric detection of C-reactive protein (CRP). Different lighting conditions and shooting angles of scenes are simulated in image acquisition, and the detection-related performance of µPADs is improved by using a machine learning algorithm. The You Only Look Once (YOLO) model is used to identify the areas of reaction in µPADs. This model can observe an image only once to predict the objects present in it and their locations. The YOLO model trained in this study was able to identify all the reaction areas quickly without incurring any error. These reaction areas were categorized by classification algorithms to determine the risk level of CRP concentration. Multi-layer perceptron, convolutional neural network, and residual network algorithms were used for the classification tasks, where the latter yielded the highest accuracy of 96%. It has a promising application prospect in fast recognition and analysis of µPADs.

Nanozyme enhanced magnetic immunoassay for dual-mode detection of gastrin-17.

In this study, we developed a novel magnetic lateral flow assay based on iron oxide decorated with platinum probes (Fe3O4@Pt) for dual-mode detection of gastrin-17 (G-17), which is one of the main biomarkers for early gastric cancer diagnosis. The probe material exhibits both magnetic properties and peroxidase activity. The peroxidase activity enhances the intensity of the brownish coloring of the Fe3O4@Pt probes on the test strip, with a limit of detection of 10 pg mL-1 using the naked eye, which is remarkable for colorimetric lateral flow assays. The magnetic property allows the simple separation and enrichment of the sample, and the signal can be read using a magnetic assay reader for quantitative detection. The linear range for G-17 using the magnetic signal was determined as 10 pg mL-1 to 2200 pg mL-1, and the calculated limit of detection was as low as 3.365 pg mL-1, thereby covering the reference range for G-17. Serum samples were used to validate the test strip, which exhibited high sensitivity, high specificity, and consistency with the results obtained by the enzyme-linked immunosorbent assay method. The entire inspection process using this method can produce results within 35 min and it is simple to operate without requiring strict experimental conditions. This dual-mode lateral flow test strip provides a simple, rapid, and quantitative strategy for detecting G-17, and it may also be valuable in other portable diagnostic applications.

Magnetic frequency mixing technological advances for the practical improvement of point-of-care testing.

Nanomaterials, especially superparamagnetic nanomaterials, have recently played essential roles in point-of-care testing due to their intrinsic magnetic, electrochemical, and optical properties. The inherent superparamagnetism of magnetic nanoparticles makes them highly sensitive for quantitative detection. Among the various magnetic detection technologies, frequency mixing technology (FMT) technology is an emerging detection technique in the nanomedical field. FMT sensors have high potential for development in the field of biomedical quantitative detection due to their simple structure, and they are not limited to the materials used. In particular, they can be applied for large-scale disease screening, early tumor marker detection, and low-dose drug detection. This review summarizes the principles of FMT and recent advances in the fields of immunoadsorption, lateral flow assay detection, magnetic imaging, and magnetic nanoparticles recognition. The advantages and limitations of FMT sensors for robust, ultrasensitive biosensing are highlighted. Finally, the future requirements and challenges in the development of this technology are described. This review provides further insights for researchers to inspire the future development of FMT by integration into biosensing and devices with a broad field of applications in analytical sensing and clinical usage.

Recent developments in sensors for wearable device applications.

Wearable devices are a new means of human-computer interaction with different functions, underlying principles, and forms. They have been widely used in the medical and health fields, in applications including physiological signal monitoring; sports; and environmental detection, while subtly affecting people's lives and work. Wearable sensors as functional components of wearable devices have become a research focus. In this review, we systematically summarize recent progress in the development of wearable sensors and related devices. Wearable sensors in medical health applications, according to the principle of measurement, are divided into physical and chemical quantity detection. These sensors can monitor and measure specific parameters, thereby enabling continuously improvements in the quality and feasibility of medical treatment. Through the detection of human movement, such as breathing, heartbeat, or bending, wearable sensors can evaluate body movement and monitor an individual's physical performance and health status. Wearable devices detecting aspects of the environment while maintaining high adaptability to the human body can be used to evaluate environmental quality and obtain more accurate environmental information. The ultimate goal of this review is to provide new insights and directions for the future development and broader application of wearable devices in various fields.Graphical abstract.

Super-Resolution Velocity Reconstruction and Control of Ultrasonic Motors.

Accurate and fast velocity feedback signal is essential for the velocity control of ultrasonic motors (USMs). However, the low operating velocity of USMs results in a long velocity detection dead time (VDDT) of incremental encoders, which seriously restricts the dynamic control performance of USMs. Therefore, this article presents a super-resolution velocity control (SRVC) scheme based on the velocity reconstruction for the USM. First, the mathematical model of the USM is derived from the mechanical characteristics and the electromechanical coupling characteristics. Then, the velocity reconstruction method is proposed by combining the model estimated velocity and the encoder measured velocity. The closed-loop control scheme using the reconstructed velocity is implemented by a self-designed driving circuit. Experimental results show the velocity reconstruction method not only can break through the limitation of the encoder resolution to reduce the VDDT but also has a high-velocity accuracy. Furthermore, compared with the existing encoder-based control scheme, the proposed SRVC scheme has a faster velocity response under different loads.

Rapid developments in lateral flow immunoassay for nucleic acid detection.

Recently, lateral flow assay (LFA) for nucleic acid detection has drawn increasing attention in the point-of-care testing fields. Due to its rapidity, easy implementation, and low equipment requirement, it is well suited for use in rapid diagnosis, food authentication, and environmental monitoring under source-limited conditions. This review will discuss two main research directions of lateral flow nucleic acid tests. The first one is the incorporation of isothermal amplification methods with LFA, which ensures an ultra-high testing sensitivity under non-laboratory conditions. The two most commonly used methodologies will be discussed, namely Loop-mediated Isothermal Amplification (LAMP) and Recombinase Polymerase Amplification (RPA), and some novel methods with special properties will also be introduced. The second research direction is the development of novel labeling materials. It endeavors to increase the sensitivity and quantifiability of LFA testing, where signals can be read and analyzed by portable devices. These methods are compared in terms of limits of detection, detection times, and quantifiabilities. It is anticipated that future research on lateral flow nucleic acid tests will focus on the integration of the whole testing process into a microfluidic system and the combination with molecular diagnostic tools such as clustered regularly interspaced short palindromic repeats to facilitate a rapid and accurate test.

Integrating Epigenetic Modulators in Nanofibers for Synergistic Gastric Cancer Therapy via Epigenetic Reprogramming.

Epigenetic dysregulations resulting from the defects of epigenetic regulators are often reversible in tumorigenesis, making them promising cancer therapeutic targets. However, the limited specificity of action, short-term stability, and low retention of the epigenetic drugs greatly impede their clinical efficacy against solid tumors. Herein a method of combinatorial delivery of epigenetic modulatory drugs via a molecular self-assembly strategy was developed using inhibitors of DNA methyltransferases and histone deacetylases. The drug-drug conjugates can self-assemble into nanofibers with enhanced chemical stability. The nanofibers synergistically regulate aberrant DNA methylation and histone deacetylation, subsequently reprogram the gene expression profiles, and finally inhibit gastric cancer cell proliferation and promote cell apoptosis. The superior in vivo therapeutic efficacy of the nanofibers could be ascribed to the prolonged retention and accumulation in tumors and the minimized off-target effects. Therefore, this design of epigenetic-drug-based nanofiber formulation may provide a valuable paradigm for cancer therapy through epigenetic reprogramming.