Dual-band terahertz metamaterial sensor and its sensing capacity enhanced with a central-relief design.

In this paper, a dual-band terahertz metamaterial sensor based on aluminum and silicon is proposed and simulated. The aluminum surface, which is deposited on a silicon substrate, is made of a C-shaped frame resonator, a rectangular beam, and a cross. The device is insensitive to the change of incident angle in the range of 0°-30°, which shows the great transmission stability of the sensor. By examining the resonance frequency shift, it is shown that 98.3 and 237.5 GHz/RIU refractive index sensitivity can be obtained near 1.76 and 2.404 THz transmission dips of the proposed structure, respectively. The two dips can be used to sense analytes in different refractive index ranges, respectively. For Dip 1 at 1.76 THz, the range is 1.0-1.6. For Dip 2 at 2.404 THz, the range is 1.6-2.0. Different from traditional multi-band metamaterial sensors, two dips generated by the proposed device can measure continuous and non-multiplexed refractive index ranges, respectively. Because the resonance frequencies of matters are different, such a characteristic enables the device to measure different types of analyte using the appropriate resonant peak. A central-relief design is then proposed based on perturbation theory to further improve its sensing performance. The aluminum cross is covered by polyimide, which can interfere with the scattering field on the metal surface and affect the transmission results. For both transmission dips, the optimized structure realizes higher sensitivities of 111.7 GHz/RIU and 262.5 GHz/RIU, respectively. More significantly, the optimized structure also has the characteristic of a wide and non-multiplexed refractive index range. In addition, the effects of analyte thickness and polyimide layer thickness on sensor performance are also discussed. The proposed structure opens up new prospects in the design of multiple-band terahertz metamaterial sensors. It can also meet the sensing needs of biomedical, environmental monitoring, and industrial manufacturing.

A tri-functional, independently tunable terahertz absorber based on a vanadium dioxide-graphene hybrid structure.

This paper proposes a simulated design for a versatile terahertz absorber that can be actively tuned. The absorber utilizes the unique tuning capabilities of graphene and vanadium dioxide, enabling it to alternate between ultra-broadband absorption, broadband absorption, and almost complete reflection. In the metallic phase of vanadium dioxide, coupled with a graphene Fermi level at 0 eV, the absorber achieves ultra-broadband absorption. This spans an extensive frequency range from 3.85 THz to 9.73 THz, exhibiting an absorption rate surpassing 90%. As we shift to the insulating phase of vanadium dioxide and adjust the graphene Fermi level to 1 eV, the absorber operates in a broadband absorption mode. This mode spans 2.98 THz to 4.63 THz, demonstrating an absorption rate exceeding 90%. In the insulating state of vanadium dioxide with a graphene Fermi level at 0 eV, the absorber metamorphoses into a nearly total reflector. Its maximum absorption rate is a mere 0.52%. The unique adjustability of vanadium dioxide and graphene independently enables the fine-tuning of absorption rates for both ultra-broadband and broadband absorption without encountering interference. Additionally, thanks to the central symmetry inherent in the proposed structure, the absorber exhibits insensitivity to alterations in polarization angles and remains stable under a broad range of incident angles. With these benefits, the absorber shows promising potential for applications in electromagnetic stealth, wireless communication, and so on.

A dual ultra-broadband switchable high-performance terahertz absorber based on hybrid graphene and vanadium dioxide.

A tunable dual broadband switchable terahertz absorber based on vanadium dioxide and graphene is proposed. The tunability of graphene and the phase transition properties of vanadium dioxide are used to switch broadband absorption between low-frequency and high-frequency, as well as the absorption rate tuning function. The simulation results indicate that when vanadium dioxide is in the insulating phase and the graphene Fermi energy is 0.7 eV, the absorber achieves low-frequency broadband absorption within the range of 2.6-4.2 THz with an absorptance greater than 90%; when vanadium dioxide is in the metallic phase and the graphene Fermi energy is 0 eV, the absorber achieves high-frequency broadband absorption within the range of 4.9-10 THz with an absorptance greater than 90%. Furthermore, the absorptance can be tuned by adjusting the conductivity of vanadium dioxide or the Fermi energy of graphene. Due to the central symmetry of the proposed structure, the absorber is completely insensitive to polarization. For TE and TM polarized waves, both low and high-frequency broadband absorption are maintained over a range of incident angles from 0° to 50°. The simple structure, tunable absorption rate, insensitivity to polarization angle and incident angle properties are advantages of our proposed absorber. It has broad application prospects in adjustable filters and electromagnetic shielding.

Flat Photonic Crystal Fiber Plasmonic Sensor for Simultaneous Measurement of Temperature and Refractive Index with High Sensitivity.

A compact temperature-refractive index (RI) flat photonic crystal fiber (PCF) sensor based on surface plasmon resonance (SPR) is presented in this paper. Sensing of temperature and RI takes place in the x- and y- polarization, respectively, to avoid the sensing crossover, eliminating the need for matrix calculation. Simultaneous detection of dual parameters can be implemented by monitoring the loss spectrum of core modes in two polarizations. Compared with the reported multi-function sensors, the designed PCF sensor provides higher sensitivities for both RI and temperature detection. A maximum wavelength sensitivity of -5 nm/°C is achieved in the temperature range of -30-40 °C. An excellent optimal wavelength sensitivity of 17,000 nm/RIU is accomplished in the RI range of 1.32-1.41. The best amplitude sensitivity of RI is up to 354.39 RIU-1. The resolution of RI and temperature sensing is 5.88 × 10-6 RIU and 0.02 °C, respectively. The highest value of the figure of merit (FOM) is 216.74 RIU-1. In addition, the flat polishing area of the gold layer reduces the manufacturing difficulty. The proposed sensor has the characteristics of high sensitivity, simple structure, good fabrication repeatability, and flexible operation. It has potential in medical diagnosis, chemical inspection, and many other fields.

Tunable wideband-narrowband switchable absorber based on vanadium dioxide and graphene.

A functionally tunable and absorption-tunable terahertz (THz) metamaterial absorber based on vanadium dioxide (VO2) and graphene is proposed and verified numerically. Based on phase transition properties of VO2 and tunability of graphene, the switching performance between ultra-broadband and narrow-band near-perfect absorption can be achieved. We simulate and analyze the characteristics of the constructed model by finite element analysis. Theoretical calculations show that when VO2 is in the metallic state and the graphene Fermi energy is 0 eV, the designed absorber can perform ultra-broadband absorption. The absorber achieves greater than 95% absorption in the 2.85 - 10THz range. When VO2 is in the insulating state and the graphene Fermi energy is 0.7 eV, more than 99.5% absorption can be achieved at 2.3 THz. The absorption rate can be tuned by changing the conductivity of VO2 and the Fermi energy of graphene. Moreover, the proposed absorber displays good polarization insensitivity and wide incident angle stability. The design may have potential applications in terahertz imaging, sensing, electromagnetic shielding and so on.

Determination of Alzheimer biomarker DNA by using an electrode modified with in-situ precipitated molybdophosphate catalyzed by alkaline phosphatase-encapsulated DNA hydrogel and target recycling amplification.

An electrochemical biosensor is described for highly sensitive determination of tDNA, an Alzheimer's disease (AD)-related biomarker. Electroactive molybdophosphate anions were precipitated in-situ on a glassy carbon electrode (GCE) via catalytic hydrolysis by alkaline phosphatase (ALP). This is followed by recycling amplification of tDNA. Four DNA strands (referred to as S1, S2, S3 and S4) were designed to assemble X-shape DNA (X-DNA) building blocks. These were further extended into four directions under the action of DNA polymerase. The resultant two X-DNA motifs were polymerize. Simultaneously, ALP is encapsulated into a hydrogels network to obtain a porous material of type ALP@DNAhg. The GCE was modified with reduced graphene oxide functionalized with gold nanoparticles (Au@rGO). If ALP@DNAhg are captured via strand displacement, tDNA recycling assembly for signal amplification is initiated. This results in the immobilization of large amounts of ALP. On introduction of pyrophosphate and molybdate (MoO42-), ALP will catalyze the hydrolysis of pyrophosphate to produce phosphate. It will react with molybdate to form redox active phosphomolybdate anions (PMo12O403-). Its amperometrical signal depends on the concentration of tDNA in the 1.0 × 10-2 to 1.0 × 104 pM concentration range, and the detection limit is 3.4 × 10-3 pM. Graphical abstract Schematic presentation of (a) preparation of alkaline phosphatase-encapsulated DNA hydrogel (ALP@DNAhg). (b) fabrication of the biosensor for target DNA (tDNA) based on ALP@DNAhg to catalyze in situ precipitation of electroactive molybdophosphate anion (PMo12O403-) and tDNA recycling amplification, achieving tDNA-dependent electrochemical signal readout (X-DNA: X-shape DNA building block. TdT: terminal deoxynucleotidyl transferase. dATP: deoxyadenosine triphosphate. dTTP: deoxythymidine triphosphate. X-DNA-pAn and X-DNA-pTn: X-DNA motifs with poly-A and poly-T tails. ALP: alkaline phosphatase. ALP@DNAhg: ALP-encapsulated DNA hydrogels. Au@rGO: gold nanoparticles-functionalized reduced graphene oxide. GCE: glass carbon electrode. HP1, 2: hairpin DNA 1, 2. MCH: 6-mercaptohexanol. tDNA: target DNA. CV: cyclic voltammetry).

Integrating Temporal Pattern Mining in Ischemic Stroke Prediction and Treatment Pathway Discovery for Atrial Fibrillation.

Atrial fibrillation (AF) is associated with an increased risk of acute ischemic stroke (AIS). Accurately predicting AIS and planning effective treatment pathways for AIS prevention are crucial for AF patients. Because of the temporality of patients' disease progressions, sequential disease and treatment patterns have the potential to improve risk prediction performance and contribute to effective treatment pathways. This paper integrates temporal pattern mining into the AF study of AIS prediction and treatment pathway discovery. We combine temporal pattern mining with feature selection to identify temporal risk factors that have predictive ability, and integrate temporal pattern mining with treatment efficacy analysis to discover temporal treatment patterns that are statistically effective. Results show that our approach has identified new potential temporal risk factors for AIS that can improve the prediction performance, and has discovered treatment pathway patterns that are statistically effective to prevent AIS for AF patients.

Glucose oxidase-initiated cascade catalysis for sensitive impedimetric aptasensor based on metal-organic frameworks functionalized with Pt nanoparticles and hemin/G-quadruplex as mimicking peroxidases.

Based on cascade catalysis amplification driven by glucose oxidase (GOx), a sensitive electrochemical impedimetric aptasensor for protein (carcinoembryonic antigen, CEA as tested model) was proposed by using Cu-based metal-organic frameworks functionalized with Pt nanoparticles, aptamer, hemin and GOx (Pt@CuMOFs-hGq-GOx). CEA aptamer loaded onto Pt@CuMOFs was bound with hemin to form hemin@G-quadruplex (hGq) with mimicking peroxidase activity. Through sandwich-type reaction of target CEA and CEA aptamers (Apt1 and Apt2), the obtained Pt@CuMOFs-hGq-GOx as signal transduction probes (STPs) was captured to the modified electrode interface. When 3,3-diaminobenzidine (DAB) and glucose were introduced, the cascade reaction was initiated by GOx to catalyze the oxidation of glucose, in situ generating H2O2. Simultaneously, the decomposition of the generated H2O2 was greatly promoted by Pt@CuMOFs and hGq as synergistic peroxide catalysts, accompanying with the significant oxidation process of DAB and the formation of nonconductive insoluble precipitates (IPs). As a result, the electron transfer in the resultant sensing interface was effectively hindered and the electrochemical impedimetric signal (EIS) was efficiently amplified. Thus, the high sensitivity of the proposed CEA aptasensor was successfully improved with 0.023pgmL-1, which may be promising and potential in assaying certain clinical disease related to CEA.

A sub-picomolar assay for protein by using cubic Cu2O nanocages loaded with Au nanoparticles as robust redox probes and efficient non-enzymatic electrocatalysts.

In this work, a simple and sensitive electrochemical aptasensor for protein (thrombin - TB used as the model) was developed by using cubic Cu2O nanocages (Cu2O-NCs) loaded with Au nanoparticles (AuNPs@Cu2O-NCs) as non-enzymatic electrocatalysts and robust redox probes. Through the specific sandwich-type reaction between TB and TB aptamers (TBA), the formed AuNPs@Cu2O-NCs bound with NH2-TBA were captured onto the electrode surface modified with SH-TBA. Based on the inherent redox activity of AuNPs@Cu2O-NCs with cubic nanostructures, a detectable electrochemical signal was generated which was dependent on the analyte concentration. Meanwhile, AuNPs@Cu2O-NCs showed an efficient electrocatalytic capability in the reduction of H2O2, resulting in a significant enhancement of the response signal. Thus, the simplification of the proposed strategy and the improvement of analytical performances were easily achieved with a sub-picomolar sensitivity (the limit of detection was 0.066 pmol L-1). The applicability of the simple and sensitive aptasensor was successfully demonstrated by assaying TB in human serum samples. This non-enzymatic detection platform would be potential and promising in clinical diagnostics and protein analysis techniques.

Group-Based Trajectory Analysis for Long-Term Use of Warfarin Therapy in Atrial Fibrillation Patients.

Atrial fibrillation (AF) patients suffer a high risk of ischemic stroke and other thromboembolism (TE). Warfarin is a long-term oral medication and is effective in reducing TE for AF patients. Identifying the trajectory patterns of warfarin use in AF patients and discovering how different trajectories are associated with different TE outcomes are important for understanding long-term use of warfarin. Also, finding the factors affecting future warfarin use and predicting the warfarin use trajectory for new patients can help to efficiently target the specific patient groups and propose relevant interventions for warfarin use. This paper, combining group-based trajectory modeling and predictive modeling, has successfully discovered three patient groups with distinct warfarin use trajectories. Also, results suggest that the warfarin use trajectory has potential association with the TE outcomes. Moreover, results show factors affecting the future trajectory, which have been used to build prediction models for the warfarin use group.

Integrated Machine Learning Approaches for Predicting Ischemic Stroke and Thromboembolism in Atrial Fibrillation.

Atrial fibrillation (AF) is a common cardiac rhythm disorder, which increases the risk of ischemic stroke and other thromboembolism (TE). Accurate prediction of TE is highly valuable for early intervention to AF patients. However, the prediction performance of previous TE risk models for AF is not satisfactory. In this study, we used integrated machine learning and data mining approaches to build 2-year TE prediction models for AF from Chinese Atrial Fibrillation Registry data. We first performed data cleansing and imputation on the raw data to generate available dataset. Then a series of feature construction and selection methods were used to identify predictive risk factors, based on which supervised learning methods were applied to build the prediction models. The experimental results show that our approach can achieve higher prediction performance (AUC: 0.71~0.74) than previous TE prediction models for AF (AUC: 0.66~0.69), and identify new potential risk factors as well.

A system dynamics approach to analyze laboratory test errors.

Although many researches have been carried out to analyze laboratory test errors during the last decade, it still lacks a systemic view of study, especially to trace errors during test process and evaluate potential interventions. This study implements system dynamics modeling into laboratory errors to trace the laboratory error flows and to simulate the system behaviors while changing internal variable values. The change of the variables may reflect a change in demand or a proposed intervention. A review of literature on laboratory test errors was given and provided as the main data source for the system dynamics model. Three "what if" scenarios were selected for testing the model. System behaviors were observed and compared under different scenarios over a period of time. The results suggest system dynamics modeling has potential effectiveness of helping to understand laboratory errors, observe model behaviours, and provide a risk-free simulation experiments for possible strategies.

Modelling clinical diagnostic errors: a system dynamics approach.

The diagnostic process involves a series of stages in the patient pathway. Any errors or misleading information from any stage could lead to errors in the final decision-making. System dynamics modeling maps the diagnostic process as a whole and seeks to provide a quantitative way of analyzing different errors at each stage as well as relevant key factors. This paper provides a framework based on system dynamics for modeling the tracing of errors inside of the system from where errors initially occur, the routes of errors inside of the system, and how errors are delivered out of the system. Also, a detailed illustration of the phase history and physical examinations is provided as an example to explain how relevant factors can be interpreted and how this affects errors according to the framework.

A causal loop approach to the study of diagnostic errors.

Missed, wrong or delayed diagnosis has a direct effect on patient safety. Diagnostic errors have been discussed at length, however it still lacks a systematic approach. This study proposed a more systematic way of studying diagnostic errors by using a causal loop diagram. A systematic review was used to find the key factors which may cause diagnostic errors and their interrelationships. A causal loop diagram, as a qualitative model at the first stage of system dynamics modeling, was produced to map all the factor and interrelationships. The diagram provides not only the direct and indirect factors affecting correct diagnosis, but also a clear view of how the change of one factor in the model triggers changes of other factors and then the change of the number of final diagnostic errors.