Mackmchugh1479

The developed LFIA provided highly sensitive and rapid (8 min) point-of-need testing.Smartwatches have the potential to support health care in everyday life by supporting self-monitoring of health conditions and personal activities. This paper aims to develop a model that predicts the prevalence of cardiovascular disease using health-related data that can be easily measured by smartwatch users. To this end, the data corresponding to the health-related data variables provided by the smartwatch are selected from the Korea National Health and Nutrition Examination Survey. To classify the prevalence of cardiovascular disease with these selected variables, we apply logistic regression, artificial neural network, and support vector machine among machine learning classification techniques, and compare the appropriateness of the algorithm through classification performance indicators. The prediction model using support vector machine showed the highest accuracy. Next, we analyze which structures or parameters of the support vector machine contribute to increasing accuracy and derive the importance of input variables. Since it is very important to diagnose cardiovascular disease early correctly, we expect that this model will be very useful if there is a tool to predict whether cardiovascular disease develops or not.This research presents an electrochemical immunosensor for collagen I detection using a self-assembled monolayer (SAM) of gold nanoparticles (AuNPs) and covalently immobilized half-reduced monoclonal antibody as a receptor; this allowed for the validation of the collagen I concentration through two different independent methods electrochemically by Electrochemical Impedance Spectroscopy (EIS), and optically by Surface Plasmon Resonance (SPR). The high unique advantage of the proposed sensor is based on the performance of the stable covalent immobilization of the AuNPs and enzymatically reduced half-IgG collagen I antibodies, which ensured their appropriate orientation onto the sensor's surface, good stability, and sensitivity properties. The detection of collagen type I was performed in a concentration range from 1 to 5 pg/mL. Moreover, SPR was utilized to confirm the immobilization of the monoclonal half-antibodies and sensing of collagen I versus time. Furthermore, EIS experiments revealed a limit of detection (LOD) of 0.38 pg/mL. The selectivity of the performed immunosensor was confirmed by negligible responses for BSA. The performed approach of the immunosensor is a novel, innovative attempt that enables the detection of collagen I with very high sensitivity in the range of pg/mL, which is significantly lower than the commonly used enzyme-linked immunosorbent assay (ELISA).Optofluidic flow-through biosensors are being developed for single particle detection, particularly as a tool for pathogen diagnosis. The sensitivity of the biosensor chip depends on design parameters, illumination format (side vs. top), and flow configuration (parabolic, two- and three-dimensional hydrodynamic focused (2DHF and 3DHF)). AZD9291 We study the signal differences between various combinations of these design aspects. Our model is validated against a sample of physical devices. We find that side-illumination with 3DHF produces the strongest and consistent signal, but parabolic flow devices process a sample volume more quickly. Practical matters of optical alignment are also discussed, which may affect design choice.This paper presents a review on the biomedical applications of electromagnetic detection in recent years. First of all, the thermal, non-thermal, and cumulative thermal effects of electromagnetic field on organism and their biological mechanisms are introduced. According to the electromagnetic biological theory, the main parameters affecting electromagnetic biological effects are frequency and intensity. This review subsequently makes a brief review about the related biomedical application of electromagnetic detection and biosensors using frequency as a clue, such as health monitoring, food preservation, and disease treatment. In addition, electromagnetic detection in combination with machine learning (ML) technology has been used in clinical diagnosis because of its powerful feature extraction capabilities. Therefore, the relevant research involving the application of ML technology to electromagnetic medical images are summarized. Finally, the future development to electromagnetic detection for biomedical applications are presented.The availability of antigen tests for SARS-CoV-2 represents a major step for the mass surveillance of the incidence of infection, especially regarding COVID-19 asymptomatic and/or early-stage patients. Recently, we reported the development of a Bioelectric Recognition Assay-based biosensor able to detect the SARS-CoV-2 S1 spike protein expressed on the surface of the virus in just three minutes, with high sensitivity and selectivity. The working principle was established by measuring the change of the electric potential of membrane-engineered mammalian cells bearing the human chimeric spike S1 antibody after attachment of the respective viral protein. In the present study, we applied the novel biosensor to patient-derived nasopharyngeal samples in a clinical set-up, with absolutely no sample pretreatment. More importantly, membrane-engineered cells were pre-immobilized in a proprietary biomatrix, thus enabling their long-term preservation prior to use as well as significantly increasing their ease-of-handle as test consumables. The plug-and-apply novel biosensor was able to detect the virus in positive samples with a 92.8% success rate compared to RT-PCR. No false negative results were recorded. These findings demonstrate the potential applicability of the biosensor for the early, routine mass screening of SARS-CoV-2 on a scale not yet realized.Rapid quantification of nitrite (NO2-) in food, drink and body fluids is of significant importance for both food safety and point-of-care (POA) applications. However, conventional nitrite analytical methods are complicated, constrained to sample content, and time-consuming. Inspired by a nitrite-triggered surface plasmon-assisted catalysis (SPAC) reaction, a rapid point-of-care detection salivary nitrate was developed in this work. NO2- ions can trigger the rapid conversion of p-aminothiophenol (PATP) to p,p'-dimercaptozaobenzene (DMAB) on gold nanoparticles (GNPs) under light illumination, and the emerged new bands at ca. 1140, 1390, 1432 cm-1 originating from DMAB can be used to the quantification of nitrite. Meanwhile, to make the method entirely suitable for on-site fast screen or point-of-care application, the technique is needed to be further optimized. The calibration graph for nitrates was linear in the range of 1-100 µM with a correlation coefficient of 0.9579. The limit of detection was 1 µM. The facile method could lead to a further understanding of the progression and treatment of periodontitis and to guide professionals in planning on-site campaigns to effectively control periodontal diseases.A microRNA (miRNA) detection platform composed of a rolling circle amplification (RCA) system and an allosteric deoxyribozyme system is proposed, which can detect miRNA-21 rapidly and efficiently. Padlock probe hybridization with the target miRNA is achieved through complementary base pairing and the padlock probe forms a closed circular template under the action of ligase; this circular template results in RCA. In the presence of DNA polymerase, RCA proceeds and a long chain with numerous repeating units is formed. In the presence of single-stranded DNA (H1 and H2), multi-component nucleic acid enzymes (MNAzymes) are formed that have the ability to cleave substrates. Finally, substrates containing fluorescent and quenching groups and magnesium ions are added to the system to activate the MNAzyme and the substrate cleavage reaction, thus achieving fluorescence intensity amplification. The RCA-MNAzyme system has dual signal amplification and presents a sensing platform that demonstrates broad prospects in the analysis and detection of nucleic acids.Guided-mode resonance (GMR) sensors are widely used as biosensors with the advantages of simple structure, easy detection schemes, high efficiency, and narrow linewidth. However, their applications are limited by their relatively low sensitivity ( less then 200 nm/RIU) and in turn low figure of merit (FOM, less then 100 1/RIU). Many efforts have been made to enhance the sensitivity or FOM, separately. To enhance the sensitivity and FOM simultaneously for more sensitive sensing, we proposed a metal layer-assisted double-grating (MADG) structure with the evanescent field extending to the sensing region enabled by the metal reflector layer underneath the double-grating. The influence of structural parameters was systematically investigated. Bulk sensitivity of 550.0 nm/RIU and FOM of 1571.4 1/RIU were obtained after numerical optimization. Compared with a single-grating structure, the surface sensitivity of the double-grating structure for protein adsorption increases by a factor of 2.4 times. The as-proposed MADG has a great potential to be a biosensor with high sensitivity and high accuracy.The issue of micro-plastics is becoming more and more important due to their ubiquity and the harm they cause to the human body. Therefore, evaluating the biological-physical interaction of micro-plastics with health cells has become the focus of many research efforts. This study focuses on the movement mode and low concentration detection development for micro-plastics in surface plasmon resonance (SPR). Firstly, 20-micrometer micro-plastics were prepared by grinding and filtering, and the movement mode was explored; then, the characteristics were investigated by SPR. Chromatographic analysis showed that the surface charge of micro-plastics dominated the elution time, and estrogen receptors (ERs) played a supporting role. A difference of micro-plastics in SPR sensorgram was observed, inferring the micro-plastics' movement in rolling mode on the ERs. Characteristics analysis indicated that the low particle number of micro-plastics on SPR showed a linear relationship with the response unit (RU). When ERs were immobilized on the biosensor, the force of the binding of micro-plastics to ERs under an ultra-low background was equivalent to the dissociation rate constant shown as follows PS (0.05 nM) > PVC (0.09 nM) > PE (0.14 nM). The ELISA-like magnetic beads experiment verified the specificity between ERs and micro-plastics. Therefore, by using the SPR technique, a biological-derived over-occupation of PS was found via higher binding force with ERs and longer retention time. In the future, there will be considerable potential for micro-plastics issues, such as identification in natural samples, biomarking, real-time detection in specific environments/regions and human health subject.The scavenging activity of myoglobin toward peroxynitrite (PON) was studied in meat extracts, using a new developed electrochemical method (based on cobalt phthalocyanine-modified screen-printed carbon electrode, SPCE/CoPc) and calculating kinetic parameters of PON decay (such as half-time and apparent rate constants). As reactive oxygen/nitrogen species (ROS/RNS) affect the food quality, the consumers can be negatively influenced. The discoloration, rancidity, and flavor of meat are altered in the presence of these species, such as PON. Our new highly thermically stable, cost-effective, rapid, and simple electrocatalytical method was combined with a flow injection analysis system to achieve high sensitivity (10.843 nA µM-1) at a nanomolar level LoD (400 nM), within a linear range of 3-180 µM. The proposed biosensor was fully characterized using SEM, FTIR, Raman spectroscopy, Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV), and Linear Sweep Voltammetry (LSV). These achievements were obtained due to the CoPc-mediated reduction of PON at very low potentials (around 0.