High-Sensitivity Microwave Sensor for Liquid Characterization Using a Complementary Circular Spiral Resonator.

This paper describes a low-cost, small size, and high-sensitivity microwave sensor using a Complementary Circular Spiral Resonator (CCSR), which operates at around 2.4 GHz, for identifying liquid samples and determining their dielectric constants. The proposed sensor was fabricated and tested to effectively identify different liquids commonly used in daily life and determine the concentrations of various ethanol⁻water mixtures at by measuring the resonant frequency of the CCSR. Using acrylic paint, a square channel was drawn at the most sensitive position of the microwave sensor to ensure accuracy of the experiment. To estimate the dielectric constants of the liquids under test, an approximate model was established using a High-Frequency Simulator Structure (HFSS). The results obtained agree very well with the existing data. Two parabolic equations were calculated and fitted to identify unknown liquids and determine the concentrations of ethanol⁻water mixtures. Thus, our microwave sensor provides a method with high sensitivity and low consumption of material for liquid monitoring and determination, which proves the feasibility and broad prospect of this low-cost system in industrial application.

Complementary Metamaterial Sensor for Nondestructive Evaluation of Dielectric Substrates.

In this paper, complementary metamaterial sensor is designed for nondestructive evaluation of dielectric substrates. The design concept is based on electromagnetic stored energy in the complementary circular spiral resonator (CCSR), which is concentrated in small volume near the host substrate at resonance. This energy can be employed to detect various electromagnetic properties of materials under test (MUT). Effective electric permittivity and magnetic permeability of the proposed sensor is extracted from scattering parameters. Sensitivity analysis is performed by varying the permittivity of MUT. After sensitivity analysis, a sensor is fabricated using standard PCB fabrication technique, and resonance frequency of the sensor due to interaction with different MUT is measured using vector network analyzer (AV3672series). The transcendental equation is derived for the fabricated sensor to calculate relative permittivity for unknown MUTs. This method is very simple and requires calculating only the resonant frequency, which reduces the cost and computation time.

Development and external validation of tools for categorizing diagnosis codes in international hospital data.

BACKGROUND: The Clinical Classification Software Refined (CCSR) is a tool that groups many thousands of International Classification of Diseases 10th Revision (ICD-10) diagnosis codes into approximately 500 clinically meaningful categories, simplifying analyses. However, CCSR was developed for use in the United States and may not work well with other country-specific ICD-10 coding systems. METHOD: We developed an algorithm for semi-automated matching of Canadian ICD-10 codes (ICD-10-CA) to CCSR categories using discharge diagnoses from adult admissions at 7 hospitals between Apr 1, 2010 and Dec 31, 2020, and manually validated the results. We then externally validated our approach using inpatient hospital encounters in Denmark from 2017 to 2018. KEY RESULTS: There were 383,972 Canadian hospital admissions with 5,186 distinct ICD-10-CA diagnosis codes and 1,855,837 Danish encounters with 4,612 ICD-10 diagnosis codes. Only 46.6% of Canadian codes and 49.4% of Danish codes could be directly categorized using the official CCSR tool. Our algorithm facilitated the mapping of 98.5% of all Canadian codes and 97.7% of Danish codes. Validation of our algorithm by clinicians demonstrated excellent accuracy (97.1% and 97.0% in Canadian and Danish data, respectively). Without our algorithm, many common conditions did not match directly to a CCSR category, such as 96.6% of hospital admissions for heart failure. CONCLUSION: The GEMINI CCSR matching algorithm (available as an open-source package at https://github.com/GEMINI-Medicine/gemini-ccsr) improves the categorization of Canadian and Danish ICD-10 codes into clinically coherent categories compared to the original CCSR tool. We expect this approach to generalize well to other countries and enable a wide range of research and quality measurement applications.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM).

Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

A Comparative Study on Surgical Treatment of Valvular Heart Disease between High-Volume Cardiac Centers in China and STS Data.

The goal of this study is to summarize valvular surgery data from the Chinese Cardiac Surgery Registry (CCSR) and compare it to the most recent data from the Society of Thoracic Surgeons (STS). From 2016 to 2018, a total of 34,386 cases of the seven most common valvular surgical procedures was obtained from the CCSR. We calculated the proportions of different procedures in the CCSR cohort (n = 34,386) as well as the change in operation volume for each procedure. We also compiled rates of postoperative in-hospital mortality and five major complications across all procedures. All of the results were compared to the STS data. The CCSR and STS data showed divergent trends in valvular heart disease features and operation volume. Although the proportion of MV repair in the CCSR (13.7%) data was lower than in the STS data (23.2%), it demonstrated a substantial upward trend. In terms of operation volume, the CCSR data showed an upward trend, but the STS data showed a downward trend. CCSR procedures showed lower mortality (2% vs. 2.6%), reoperation (2.8% vs. 4.3%), and permanent stroke (0.5% vs. 1.6%) rates than STS procedures but higher rates of prolonged ventilation (22.4% vs. 10.4%) and renal failure (5.6% vs. 3.2%). Valvular surgery quality in China's leading cardiac hospitals is roughly comparable to that in the United States. China, on the other hand, has some shortcomings that need improvement.

A Revised Comorbidity Model for Administrative Databases Using Clinical Classifications Software Refined Variables.

Background and objective Database research has shaped policies, identified trends, and informed healthcare guidelines for numerous disease conditions. However, despite their abundant uses and vast potential, administrative databases have several limitations. Adjusting outcomes for comorbidities is often needed during database analysis as a means of overcoming non-randomization. We sought to obtain a model for comorbidity adjustment based on Clinical Classifications Software Refined (CCSR) variables and compare this with current models. Our aim was to provide a simplified, adaptable, and accurate measure for comorbidities in the Agency for Healthcare Research and Quality (AHRQ) databases, in order to strengthen the validity of outcomes.  Methods The Nationwide Inpatient Sample (NIS) database for 2018 was the data source. We obtained the mortality rate among all included hospitalizations in the dataset. A model based on CCSR categories was mapped from disease groups in Sundararajan's adaptation of the modified Deyo's Charlson Comorbidity Index (CCI). We employed logistic regression analysis to obtain the final model using CCSR variables as binary variables. We tested the final model on the 10 most common reasons for hospitalizations. Results The model had a higher area under the curve (AUC) compared to the three modalities of the CCI studied in all the categories. Also, the model had a higher AUC compared to the Elixhauser model in 8/10 categories. However, the model did not have a higher AUC compared to a model made from stepwise backward regression analysis of the original 21-variable model. Conclusion We developed a 15-CCSR-variable model that showed good discrimination for inpatient mortality compared to prior models.