The novel three-dimensional pulse images analyzed by dynamic L-cube polynomial model.

A dynamic L-cube polynomial is proposed to analyze dynamic three-dimensional pulse images (d3DPIs), as an extension of the previous static L-cube polynomial. In this paper, a weighted least squares (WLS) method is proposed to fit the amplitude C(t) of d3DPI at four physiological key points in addition to the best fit of L-cube polynomials to the measured normal and cold-pressor-test (CPT)-induced taut 3DPIs. Compared with other two fitting functions, C(t) of a dynamic L-cube polynomial can be well matched by the proposed WLS method with the least relative error at four physiological key points in one beat with statistical significance, in addition to the best fit of the measured 3DPIs. Therefore, a dynamic L-cube polynomial can reflect dynamic time characteristics of normal and CPT-induced hypertensive taut 3DPIs, which can be used as an evidence of hypertension diagnosis.

Identification of exacerbation risk in patients with liver dysfunction using machine learning algorithms.

The prediction of the liver failure (LF) and its proper diagnosis would lead to a reduction in the complications of the disease and prevents the progress of the disease. To improve the treatment of LF patients and reduce the cost of treatment, we build a machine learning model to forecast whether a patient would deteriorate after admission to the hospital. First, a total of 348 LF patients were included from May 2011 to March 2018 retrospectively in this study. Then, 15 key clinical indicators are selected as the input of the machine learning algorithm. Finally, machine learning and the Model for End-Stage Liver Disease (MELD) are used to forecast the LF deterioration. The area under the receiver operating characteristic (AUC) of MELD, GLMs, CART, SVM and NNET with 10 fold-cross validation was 0.670, 0.554, 0.794, 0.853 and 0.912 respectively. Additionally, the accuracy of MELD, GLMs, CART, SVM and NNET was 0.669, 0.456, 0.794, 0.853 and 0.912. The predictive performance of the developed machine model execept the GLMs exceeds the classic MELD model. The machine learning method could support the physicians to trigger the initiation of timely treatment for the LD patients.

A memory optimization method combined with adaptive time-step method for cardiac cell simulation based on multi-GPU.

Cardiac electrophysiological simulation is a very complex computational process, which can be run on graphics processing unit (GPU) to save computational cost greatly. The use of adaptive time-step can further effectively speed up the simulation of heart cells. However, if the adaptive time-step method applies to GPU, it suffers synchronization problem on GPU, weakening the acceleration of adaptive time-step method. The previous work ran on a single GPU with the adaptive time-step to get only 1.5 times (× 1.5) faster than the fixed time-step. This study proposes a memory allocation method, which can effectively implement the adaptive time-step method on GPU. The proposed method mainly focuses on the stimulus point and potential memory arrangement in order to achieve optimal memory storage efficiency. All calculation is implemented on GPU. Large matrices such as potential are arranged in column order, and the cells on the left are stimulated. The Luo-Rudy passive (LR1) and dynamic (LRd) ventricular action potential models are used with adaptive time-step methods, such as the traditional hybrid method (THM) and Chen-Chen-Luo's (CCL) "quadratic adaptive algorithm" method. As LR1 is solved by the THM or CCL on a single GPU, the acceleration is × 34 and × 75 respectively compared with the fixed time-step. With 2 or 4 GPUs, the acceleration of the THM and CCL is × 34 or × 35 and × 73 or × 75, but it would decrease to × 5 or × 3 and × 20 or × 15 without optimization. In an LRd model, the acceleration reaches × 27 or × 85 as solved by the THM or CCL compared with the fixed time-step on multi-GPU with linear speed up increase versus the number of GPU. However, with the increase of GPUs number, the acceleration of the THM and CCL is continuously weakened before optimization. The mixed root mean square error (MRMSE) lower than 5% is applied to ensure the accuracy of simulation. The result shows that the proposed memory arrangement method can save computational cost a lot to speed up the heart simulation greatly. Graphical abstract Acceleration ratio compared with CPU with fixed time-step (dt = 0.001 ms).

Combination of "generalized Trotter operator splitting" and "quadratic adaptive algorithm" method for tradeoff among speedup, stability, and accuracy in the Markov chain model of sodium ion channels in the ventricular cell model.

The fast hybrid operator splitting (HOS) and stable uniformization (UNI) methods have been proposed to save computation cost and enhance stability for Markov chain model in cardiac cell simulations. Moreover, Chen-Chen-Luo's quadratic adaptive algorithm (CCL) combined with HOS or UNI was used to improve the tradeoff between speedup and stability, but without considering accuracy. To compromise among stability, acceleration, and accuracy, we propose a generalized Trotter operator splitting (GTOS) method combined with CCL independent of the asymptotic property of a particular ion-channel model. Due to the accuracy underestimation of the mixed root mean square error (MRMSE) method, threshold root mean square error (TRMSE) is proposed to evaluate computation accuracy. With the fixed time-step RK4 as a reference, the second-order GTOS combined with CCL (30.8-fold speedup) for the wild-type Markov chain model with nine states (WT-9 model) or (7.4-fold) for the wild-type Markov chain model with eight states (WT-8 model) is faster than UNI combined with CCL (15.6-fold) for WT-9 model or (1.2-fold) for WT-8 model, separately. Besides, the second-order GTOS combined with CCL has 3.81% TRMSE for WT-9 model or 4.32% TRMSE for WT-8 model more accurate than 72.43% TRMSE for WT-9 model or 136.17% TRMSE for WT-8 model of HOS combined with CCL. To compromise speedup and accuracy, low-order GTOS combined with CCL is suggested to have the advantages of high precision and low computation cost. For high-accuracy requirements, high-order GTOS combined with CCL is recommended. Graphical abstract.

Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets. RESULTS: Here, we have developed a novel method to impute missing gene expression data from DNA methylation data through a transfer learning-based neural network, namely, TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with a 7-11% improvement under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation-driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purposes by an independent test on the Wilms tumor dataset from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. CONCLUSIONS: TDimpute is an effective method for RNA-seq imputation with limited training samples.

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study.

BACKGROUND: The overcrowding of hospital outpatient and emergency departments (OEDs) due to chronic respiratory diseases in certain weather or under certain environmental pollution conditions results in the degradation in quality of medical care, and even limits its availability. OBJECTIVE: To help OED managers to schedule medical resource allocation during times of excessive health care demands after short-term fluctuations in air pollution and weather, we employed machine learning (ML) methods to predict the peak OED arrivals of patients with chronic respiratory diseases. METHODS: In this paper, we first identified 13,218 visits from patients with chronic respiratory diseases to OEDs in hospitals from January 1, 2016, to December 31, 2017. Then, we divided the data into three datasets: weather-based visits, air quality-based visits, and weather air quality-based visits. Finally, we developed ML methods to predict the peak event (peak demand days) of patients with chronic respiratory diseases (eg, asthma, respiratory infection, and chronic obstructive pulmonary disease) visiting OEDs on the three weather data and environmental pollution datasets in Guangzhou, China. RESULTS: The adaptive boosting-based neural networks, tree bag, and random forest achieved the biggest receiver operating characteristic area under the curve, 0.698, 0.714, and 0.809, on the air quality dataset, the weather dataset, and weather air quality dataset, respectively. Overall, random forests reached the best classification prediction performance. CONCLUSIONS: The proposed ML methods may act as a useful tool to adapt medical services in advance by predicting the peak of OED arrivals. Further, the developed ML methods are generic enough to cope with similar medical scenarios, provided that the data is available.

A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators.

Patients with chronic obstructive pulmonary disease (COPD) repeat acute exacerbations (AE). Global Initiative for Chronic Obstructive Lung Disease (GOLD) is only available for patients in stable phase. Currently, there is a lack of assessment and prediction methods for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients during hospitalization. To enhance the monitoring and treatment of AECOPD patients, we develop a novel C5.0 decision tree classifier to predict the prognosis of AECOPD hospitalized patients with objective clinical indicators. The medical records of 410 hospitalized AECOPD patients are collected and 28 features including vital signs, medical history, comorbidities and various inflammatory indicators are selected. The overall accuracy of the proposed C5.0 decision tree classifier is 80.3% (65 out of 81 participants) with 95% Confidence Interval (CI):(0.6991, 0.8827) and Kappa 0.6054. In addition, the performance of the model constructed by C5.0 exceeds the C4.5, classification and regression tree (CART) model and the iterative dichotomiser 3 (ID3) model. The C5.0 decision tree classifier helps respiratory physicians to assess the severity of the patient early, thereby guiding the treatment strategy and improving the prognosis of patients.

Fourier Series Analysis for Novel Spatiotemporal Pulse Waves: Normal, Taut, and Slippery Pulse Images.

In this article, a three-dimensional pulse image (3DPI) instead of a one-dimensional temporal pulse wave is studied to elucidate its spatiotemporal characteristics. To check the spatial and temporal properties of 3DPI, adopted is Fourier series, in which a ratio (r) is defined as one amplitude divided by the sum of the first three amplitudes of harmonics. A ratio sequence is constituted from 70 to 90 ratios in a heartbeat with 70-90 3DPIs by sampling. Twenty-four subjects (14 males and 10 females with age of 22.2 ± 3.7 years, 20.4 ± 1.4 BMI, and 112.1 ± 4.7 mmHg systolic blood pressure) are involved in this research. There are significant statistical differences in the groups of the normal, taut, and slippery 3DPIs by the first harmonic ratio average ( r 1 ¯ ) and ratio difference (Δr 1) produced from the ratio sequence. The proposed method of this study gives us a novel viewpoint to clarify the spatiotemporal characteristics of pulse images, which can translate and quantize the pulse feeling in Chinese medicine texts.

Two-electrode-pair electrocardiogram with no common ground between two pairs.

An electrocardiogram (ECG) records the electrical activity of the heart using two electrodes to detect electrophysiological signals from the organ and one reference electrode (typically attached to the right leg) as the reference potential. An inconvenience of the three electrodes is that the grounds of the amplifiers are interconnected all over the body. To address this inconvenience, this paper proposes a two-electrode ECG measurement method with common electrodes instead of the reference electrode of the right leg. The power supply and the ground of the amplifier supplying the two electrode pairs are not connected and independent. Pasted on the skin with the body as the reference potential, both electrodes detect electrophysiological signals, transmitting the signals through two isolation amplifiers to an instrumentation amplifier filter and generating ECG waveforms. Both previous-stage electrode amplifier circuits of the isolation amplifiers used their respective power supplies; their grounds were unconnected to those of the power supplies. Similarly, the next-stage electrode amplifiers used their respective power supplies, with their grounds unconnected to those of the power supplies. Experiments yielded ECG signal waveforms in Leads I to III, with that of Lead I most resembling the waveforms of conventional reference electrodes. This method can be used to develop wireless ECG devices and portable ECG devices that eliminate the need for wiring to facilitate ECG measurement.

Fast Prediction of Deterioration and Death Risk in Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease Using Vital Signs and Admission History: Retrospective Cohort Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) has 2 courses with different options for medical treatment: the acute exacerbation phase and the stable phase. Stable patients can use the Global Initiative for Chronic Obstructive Lung Disease (GOLD) to guide treatment strategies. However, GOLD could not classify and guide the treatment of acute exacerbation as acute exacerbation of COPD (AECOPD) is a complex process. OBJECTIVE: This paper aimed to propose a fast severity assessment and risk prediction approach in order to strengthen monitoring and medical interventions in advance. METHODS: The proposed method uses a classification and regression tree (CART) and had been validated using the AECOPD inpatient's medical history and first measured vital signs at admission that can be collected within minutes. We identified 552 inpatients with AECOPD from February 2011 to June 2018 retrospectively and used the classifier to predict the outcome and prognosis of this hospitalization. RESULTS: The overall accuracy of the proposed CART classifier was 76.2% (83/109 participants) with 95% CI 0.67-0.84. The precision, recall, and F-measure for the mild AECOPD were 76% (50/65 participants), 82% (50/61 participants), and 0.79, respectively, and those with severe AECOPD were 75% (33/44 participants), 68% (33/48 participants), and 0.72, respectively. CONCLUSIONS: This fast prediction CART classifier for early exacerbation detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patients' health.

Compensated active noise cancellation earphone for audiometric screening tests in noisy environments.

Objective: This study investigated hearing screening tests by using a custom-designed compensated hybrid active noise cancellation (ANC) earphone and compared it with TDH39 and Audiocups audiometric earphones under conditions of quiet, 45 dB HL masking narrowband, wideband speech-shaped, and white noise. Design: The hearing screening tests were conducted to characterise the shifts of screening results under noisy conditions, and real-ear attenuations at thresholds were assessed to quantify real-ear noise reduction performance. Study sample: Twenty-four normal-hearing adults, aged 20-25 years, participated in this study. Results: The ANC earphone exhibited significantly lower/better mean screening results than those of the TDH39 earphone at 250 and 500 Hz and those of the Audiocups earphone at 250 Hz under conditions of narrowband, speech-shaped, and white noise. Compared with the TDH39 earphone at 250 and 500 Hz, applying a hybrid ANC earphone reduced the shifts in screening results by 14.2 and 12.3 dB, respectively, under the narrowband noise condition. Conclusion: This study demonstrated that the compensated hybrid ANC earphone provided lower shifts of screening results than the TDH39 and Audiocups earphones and that it was capable of screening at 250 and 500 Hz from 20 dB HL under 45 dB HL masking narrowband and wideband noise.

Combination of "quadratic adaptive algorithm" and "hybrid operator splitting" or uniformization algorithms for stability against acceleration in the Markov model of sodium ion channels in the ventricular cell model.

The Markovian model has generally been used for cardiac electrophysiological simulations. However, the Markovian model is so stiff that speeding up the computation of the algorithms with variable time-steps always results in simulation instability. In particular, the unstable simulations always occur at a low voltage rate or current change, while transition rates in the Markovian model are changing markedly. The uniformization (UNI) method allows for a Markovian model simulation with high stability but also a high computation cost. To save computation costs with variable time-steps, we propose a speed increasing idea that is a compromise to the trade-off between stability and acceleration by combining Chen-Chen-Luo's "quadratic adaptive algorithm" (CCL) method with "hybrid operator splitting" (HOS) into the solver (CCL + HOS solver). The computation cost of this CCL + HOS solver is approximately 24 times lower than the CCL + UNI solver, and the CCL + HOS solver can function 295 times faster in comparison to the HOS solver with a fixed time-step (DT). The suggested optimal solver should be CCL + HOS solver with a maximum time-step at 0.1 ms due to its high speed with low error. Additionally, the CCL method has much better performance and stability than the hybrid method in this single-cell model simulation.

Combination of multi-variable quadratic adaptive algorithm and hybrid operator splitting method for stability against acceleration in the Markov model of sodium ion channels in the ventricular cell model.

Markovian model is widely used to study cardiac electrophysiology and drug screening. Due to the stiffness of Markov model for single-cell simulation, it is prone to induce instability by using large time-steps. Hybrid operator splitting (HOS) and uniformization (UNI) methods were devised to solve Markovian models with fixed time-step. Recently, it is shown that these two methods combined with Chen-Chen-Luo's quadratic adaptive algorithm (CCL) can save markedly computation cost with adaptive time-step. However, CCL determines the time-step size solely based on the membrane potential. The voltage changes slowly to increase the step size rapidly, while the values of state variables of Markov sodium channel model still change dramatically. As a result, the system is not stable and the errors of membrane potential and sodium current exceed 5%. To resolve this problem, we propose a multi-variable CCL method (MCCL) in which state occupancies of Markov model are included with membrane potential as the control quadratic parameters to determine the time-step adaptively. Using fixed time-step RK4 as a reference, MCCL combined with HOS solver has 17.2 times speedup performance with allowable errors 0.6% for Wild-Type Na+ channel with 9 states (WT-9) model, and it got 21.1 times speedup performance with allowable errors 3.2% for WildType Na+ channel with 8 states (WT-8) model. It is concluded that MCCL can improve the simulation instability problem induced by a large time-step made with CCL especially for high stiff Markov model under allowable speed tradeoff.

Benefits of Compression Amplification in Telephone Bluetooth-Assistive Listening Devices for People with Hearing Loss.

BACKGROUND: Telephone conversation is one of the main scenarios where people with hearing loss require assistive listening devices (ALDs). Such people experience the greatest degree of difficulty during phone conversations in noisy environments. PURPOSE: This study compared the benefits of a linear scheme with a compression amplification scheme fitted with a prescription for sloping-type hearing loss implemented in a Bluetooth ALD in quiet and noisy environments. RESEARCH DESIGN: Word recognition scores (WRSs) for the Mandarin monosyllable recognition test (MMRT) and participants' satisfaction ratings were measured to serve as objective and subjective results, respectively. STUDY SAMPLE: Twelve native Mandarin speakers aged 27-68 yr with mild to moderate sensorineural hearing loss participated in this study. INTERVENTION: A compression amplification scheme with a prescription in maximizing speech intelligibility for sloping-type hearing loss was implemented in a Bluetooth ALD. DATA COLLECTION AND ANALYSIS: The MMRT WRSs of participants wearing the Bluetooth ALD were collected. Each test was conducted in a soundproof booth under quiet and 65-dBA speech noise environments. Each participant completed a satisfaction questionnaire administered by an audiologist. The collected WRSs were examined using analyses of variance and the satisfaction ratings were analyzed using Wilcoxon signed rank tests. RESULTS: The mean MMRT WRSs of the compression amplification scheme were significantly higher than those of the linear scheme (57% and 53% higher in quiet and noisy environments, respectively). The mean satisfaction ratings of both schemes were between neutral and satisfied in the quiet environment, whereas in the noisy environment, the participants were more satisfied with the compression scheme than the linear scheme. CONCLUSIONS: The results demonstrate the effective benefits of the compression amplification scheme fitted with a prescription in maximizing speech intelligibility for sloping-type hearing loss implemented in a Bluetooth ALD for people with mild to moderate hearing loss.

Benefits of incorporating the adaptive dynamic range optimization amplification scheme into an assistive listening device for people with mild or moderate hearing loss.

This study investigated whether a self-designed assistive listening device (ALD) that incorporates an adaptive dynamic range optimization (ADRO) amplification strategy can surpass a commercially available monaurally worn linear ALD, SM100. Both subjective and objective measurements were implemented. Mandarin Hearing-In-Noise Test (MHINT) scores were the objective measurement, whereas participant satisfaction was the subjective measurement. The comparison was performed in a mixed design (i.e., subjects' hearing status being mild or moderate, quiet versus noisy, and linear versus ADRO scheme). The participants were two groups of hearing-impaired subjects, nine mild and eight moderate, respectively. The results of the ADRO system revealed a significant difference in the MHINT sentence reception threshold (SRT) in noisy environments between monaurally aided and unaided conditions, whereas the linear system did not. The benchmark results showed that the ADRO scheme is effectively beneficial to people who experience mild or moderate hearing loss in noisy environments. The satisfaction rating regarding overall speech quality indicated that the participants were satisfied with the speech quality of both ADRO and linear schemes in quiet environments, and they were more satisfied with ADRO than they with the linear scheme in noisy environments.

Pro-arrhythmogenic Effects of the V141M KCNQ1 Mutation in Short QT Syndrome and Its Potential Therapeutic Targets: Insights from Modeling.

Gain-of-function mutations in the pore-forming subunit of IKs channels, KCNQ1, lead to short QT syndrome (SQTS) and lethal arrhythmias. However, how mutant IKs channels cause SQTS and the possibility of IKs-specific pharmacological treatment remain unclear. V141M KCNQ1 is a SQTS associated mutation. We studied its effect on IKs gating properties and changes in the action potentials (AP) of human ventricular myocytes. Xenopus oocytes were used to study the gating mechanisms of expressed V141M KCNQ1/KCNE1 channels. Computational models were used to simulate human APs in endocardial, mid-myocardial, and epicardial ventricular myocytes with and without ß-adrenergic stimulation. V141M KCNQ1 caused a gain-of-function in IKs characterized by increased current density, faster activation, and slower deactivation leading to IKs accumulation. V141M KCNQ1 also caused a leftward shift of the conductance-voltage curve compared to wild type (WT) IKs (V1/2 = 33.6 ± 4.0 mV for WT, and 24.0 ± 1.3 mV for heterozygous V141M). A Markov model of heterozygous V141M mutant IKs was developed and incorporated into the O'Hara-Rudy model. Compared to the WT, AP simulations demonstrated marked rate-dependent shortening of AP duration (APD) for V141M, predicting a SQTS phenotype. Transmural electrical heterogeneity was enhanced in heterozygous V141M AP simulations, especially under ß-adrenergic stimulation. Computational simulations identified specific IK1 blockade as a beneficial pharmacologic target for reducing the transmural APD heterogeneity associated with V141M KCNQ1 mutation. V141M KCNQ1 mutation shortens ventricular APs and enhances transmural APD heterogeneity under ß-adrenergic stimulation. Computational simulations identified IK1 blockers as a potential antiarrhythmic drug of choice for SQTS.

A 2.5 mW/ch, 50 Mcps, 10-Analog Channel, Adaptively Biased Read-Out Front-End IC With Low Intrinsic Timing Resolution for Single-Photon Time-of-Flight PET Applications With Time-Dependent Noise Analysis in 90 nm CMOS.

This paper presents a 10-channel time-of-flight application-specific integrated circuit (ASIC) for positron emission tomography in a 90 nm standard CMOS process. To overcome variations in channel-to-channel timing resolution caused by mismatch and process variations, adaptive biases and a digital-to-analog converter (DAC) are utilized. The main contributions of this work are as follows. First, multistage architectures reduce the total power consumption, and detection bandwidths of analog preamplifiers and comparators are increased to 1 and 1.5 GHz, respectively, relative to those in previous studies. Second, a total intrinsic electronic timing resolution of 9.71 ps root-mean-square (RMS) is achieved (13.88 ps peak and 11.8 ps average of the 10 channels in 5 ASICs). Third, the proposed architecture reduces variations in channel-to-channel timing resolution to 2.6 bits (equivalent to 4.17 ps RMS) by calibrating analog comparator threshold levels. A 181.5 ps full-width-at-half-maximum timing resolution is measured with an avalanche photo diode and a laser setup. The power consumption is 2.5 mW using 0.5 and 1.2 V power supplies. The proposed ASIC is implemented in a 90 nm TSMC CMOS process with a total area of 3.3 mm × 2.7 mm.

Quadratic adaptive algorithm for solving cardiac action potential models.

An adaptive integration method is proposed for computing cardiac action potential models accurately and efficiently. Time steps are adaptively chosen by solving a quadratic formula involving the first and second derivatives of the membrane action potential. To improve the numerical accuracy, we devise an extremum-locator (el) function to predict the local extremum when approaching the peak amplitude of the action potential. In addition, the time step restriction (tsr) technique is designed to limit the increase in time steps, and thus prevent the membrane potential from changing abruptly. The performance of the proposed method is tested using the Luo-Rudy phase 1 (LR1), dynamic (LR2), and human O'Hara-Rudy dynamic (ORd) ventricular action potential models, and the Courtemanche atrial model incorporating a Markov sodium channel model. Numerical experiments demonstrate that the action potential generated using the proposed method is more accurate than that using the traditional Hybrid method, especially near the peak region. The traditional Hybrid method may choose large time steps near to the peak region, and sometimes causes the action potential to become distorted. In contrast, the proposed new method chooses very fine time steps in the peak region, but large time steps in the smooth region, and the profiles are smoother and closer to the reference solution. In the test on the stiff Markov ionic channel model, the Hybrid blows up if the allowable time step is set to be greater than 0.1ms. In contrast, our method can adjust the time step size automatically, and is stable. Overall, the proposed method is more accurate than and as efficient as the traditional Hybrid method, especially for the human ORd model. The proposed method shows improvement for action potentials with a non-smooth morphology, and it needs further investigation to determine whether the method is helpful during propagation of the action potential.

A Portable Low-Power Acquisition System with a Urease Bioelectrochemical Sensor for Potentiometric Detection of Urea Concentrations.

This paper presents a portable low-power battery-driven bioelectrochemical signal acquisition system for urea detection. The proposed design has several advantages, including high performance, low cost, low-power consumption, and high portability. A LT1789-1 low-supply-voltage instrumentation amplifier (IA) was used to measure and amplify the open-circuit potential (OCP) between the working and reference electrodes. An MSP430 micro-controller was programmed to process and transduce the signals to the custom-developed software by ZigBee RF module in wireless mode and UART in able mode. The immobilized urease sensor was prepared by embedding urease into the polymer (aniline-co-o-phenylenediamine) polymeric matrix and then coating/depositing it onto a MEMS-fabricated Au working electrode. The linear correlation established between the urea concentration and the potentiometric change is in the urea concentrations range of 3.16 × 10(-4) to 3.16 × 10(-2) M with a sensitivity of 31.12 mV/log [M] and a precision of 0.995 (R² = 0.995). This portable device not only detects urea concentrations, but can also operate continuously with a 3.7 V rechargeab-le lithium-ion battery (500 mA·h) for at least four days. Accordingly, its use is feasible and even promising for home-care applications.