Polarization Conforms Performance Variability in Amorphous Electrodeposited Iridium Oxide pH Sensors: A Thorough Surface Chemistry Investigation.

Electrodeposited amorphous hydrated iridium oxide (IrOx) is a promising material for pH sensing due to its high sensitivity and the ease of fabrication. However, durability and variability continue to restrict the sensor's effectiveness. Variation in probe films can be seen in both performance and fabrication, but it has been found that performance variation can be controlled with potentiostatic conditioning (PC). To make proper use of this technique, the morphological and chemical changes affecting the conditioning process must be understood. Here, a thorough study of this material, after undergoing PC in a pH-sensing-relevant potential regime, was conducted by voltammetry, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Fitting of XPS data was performed, guided by raw trends in survey scans, core orbitals, and valence spectra, both XPS and UPS. The findings indicate that the PC process can repeatably control and conform performance and surface bonding to desired calibrations and distributions, respectively; PC was able to reduce sensitivity and offset ranges to as low as ±0.7 mV/pH and ±0.008 V, respectively, and repeat bonding distributions over ~2 months of sample preparation. Both Ir/O atomic ratios (shifting from 4:1 to over 4.5:1) and fitted components assigned hydroxide or oxide states based on the literature (low-voltage spectra being almost entirely with suggested hydroxide components, and high-voltage spectra almost entirely with suggested oxide components) trend across the polarization range. Self-consistent valence, core orbital, and survey quantitative trends point to a likely mechanism of ligand conversion from hydroxide to oxide, suggesting that the conditioning process enforces specific state mixtures that include both theoretical Ir(III) and Ir(IV) species, and raising the conditioning potential alters the surface species from an assumed mixture of Ir species to more oxidized Ir species.

Association between Genetic Polymorphism of SCN1A, GABRA1 and ABCB1 and Drug Responsiveness in Vietnamese Epileptic Children.

Background and Objectives: Drug resistant epilepsy (DRE) is a major hurdle in epilepsy, which hinders clinical care, patients' management and treatment outcomes. DRE may partially result from genetic variants that alter proteins responsible for drug targets and drug transporters in the brain. We aimed to examine the relationship between SCN1A, GABRA1 and ABCB1 polymorphism and drug response in epilepsy children in Vietnam. Materials and Methods: In total, 213 children diagnosed with epilepsy were recruited in this study (101 were drug responsive and 112 were drug resistant). Sanger sequencing had been performed in order to detect six single nucleotide polymorphisms (SNPs) belonging to SCN1A (rs2298771, rs3812718, rs10188577), GABRA1 (rs2279020) and ABCB1 (rs1128503, rs1045642) in study group. The link between SNPs and drug response status was examined by the Chi-squared test or the Fisher's exact test. Results: Among six investigated SNPs, two SNPs showed significant difference between the responsive and the resistant group. Among those, heterozygous genotype of SCN1A rs2298771 (AG) were at higher frequency in the resistant patients compared with responsive patients, playing as risk factor of refractory epilepsy. Conversely, the heterozygous genotype of SCN1A rs3812718 (CT) was significantly lower in the resistant compared with the responsive group. No significant association was found between the remaining four SNPs and drug response. Conclusions: Our study demonstrated a significant association between the SCN1A genetic polymorphism which increased risk of drug-resistant epilepsy in Vietnamese epileptic children. This important finding further supports the underlying molecular mechanisms of SCN1A genetic variants in the pathogenesis of drug-resistant epilepsy in children.

Concepts, electrode configuration, characterization, and data analytics of electric and electrochemical microfluidic platforms: a review.

Microfluidic cytometry (MC) and electrical impedance spectroscopy (EIS) are two important techniques in biomedical engineering. Microfluidic cytometry has been utilized in various fields such as stem cell differentiation and cancer metastasis studies, and provides a simple, label-free, real-time method for characterizing and monitoring cellular fates. The impedance microdevice, including impedance flow cytometry (IFC) and electrical impedance spectroscopy (EIS), is integrated into MC systems. IFC measures the impedance of individual cells as they flow through a microfluidic device, while EIS measures impedance changes during binding events on electrode regions. There have been significant efforts to improve and optimize these devices for both basic research and clinical applications, based on the concepts, electrode configurations, and cell fates. This review outlines the theoretical concepts, electrode engineering, and data analytics of these devices, and highlights future directions for development.

Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders.

Metabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS.

Investigation of EEG-Based Biometric Identification Using State-of-the-Art Neural Architectures on a Real-Time Raspberry Pi-Based System.

Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system.

A Low-Cost, 3D-Printed Biosensor for Rapid Detection of Escherichia coli.

Detection of bacterial pathogens is significant in the fields of food safety, medicine, and public health, just to name a few. If bacterial pathogens are not properly identified and treated promptly, they can lead to morbidity and mortality, also possibly contribute to antimicrobial resistance. Current bacterial detection methodologies rely solely on laboratory-based techniques, which are limited by long turnaround detection times, expensive costs, and risks of inadequate accuracy; also, the work requires trained specialists. Here, we describe a cost-effective and portable 3D-printed electrochemical biosensor that facilitates rapid detection of certain Escherichia coli (E. coli) strains (DH5α, BL21, TOP10, and JM109) within 15 min using 500 µL of sample, and costs only USD 2.50 per test. The sensor displayed an excellent limit of detection (LOD) of 53 cfu, limit of quantification (LOQ) of 270 cfu, and showed cross-reactivity with strains BL21 and JM109 due to shared epitopes. This advantageous diagnostic device is a strong candidate for frequent testing at point of care; it also has application in various fields and industries where pathogen detection is of interest.

Fetal Electrocardiogram Extraction from the Mother's Abdominal Signal Using the Ensemble Kalman Filter.

Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.

A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram.

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16-64 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery.

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram.

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios.

Phenotyping an adult zebrafish lamp2 cardiomyopathy model identifies mTOR inhibition as a candidate therapy.

Adult zebrafish is an emerging vertebrate model for studying genetic basis of cardiomyopathies; but whether the simple fish heart can model essential features of hypertrophic cardiomyopathy (HCM) remained unknown. Here, we report a comprehensive phenotyping of a lamp2 knockout (KO) mutant. LAMP2 encodes a lysosomal protein and is a causative gene of Danon disease that is characterized by HCM and massive autophagic vacuoles accumulation in the tissues. There is no effective therapy yet to treat this most lethal cardiomyopathy in the young. First, we did find the autophagic vacuoles accumulation in cardiac tissues from lamp2 KO. Next, through employing a set of emerging phenotyping tools, we revealed heart failure phenotypes in the lamp2 KO mutants, including decreased ventricular ejection fraction, reduced physical exercise capacity, blunted ß-adrenergic contractile response, and enlarged atrium. We also noted changes of the following indices suggesting cardiac hypertrophic remodeling in lamp2 KO: a rounded heart shape, increased end-systolic ventricular volume and density of ventricular myocardium, elevated actomyosin activation kinetics together with increased maximal isometric tension at the level of cardiac myofibrils. Lastly, we assessed the function of lysosomal-localized mTOR on the lamp2-associated Danon disease. We found that haploinsufficiency of mtor was able to normalize some characteristics of the lamp2 KO, including ejection fraction, ß-adrenergic response, and the actomyosin activation kinetics. In summary, we demonstrate the feasibility of modeling the inherited HCM in the adult zebrafish, which can be used to develop potential therapies.

Acquisition, Processing and Analysis of Electrocardiogram in Awake Zebrafish.

Long-term monitoring of intrinsic electrocardiogram (ECG) in zebrafish plays a crucial role in heart disease studies as well as drug screening. In this work, we developed a polymer-based apparatus with embedded flexible thin-film electrodes to acquire ECG signals of awake zebrafish. The apparatus was made of polydimethylsiloxane (PDMS) using the molding technique with molds formed by 3D printing. A graphical user interface (GUI) was built in National Instruments LabView platform for real-time recording, processing and analysis. The program provided important features, such as signal de-noising, characteristic wave detection and anomaly detection. Further, it could operate on both real-time coming signals as well as previously-saved data, facilitating analysis and interpretation. We demonstrated the use of our system to investigate the effects of the anesthetic drug, namely Tricaine (MS-222), on cardiac electrophysiology of zebrafish, revealing promising findings. We speculate that our novel system may contribute to a host of studies in various disciplines using the zebrafish model.

An Edge-Fog-Cloud Architecture of Streaming Analytics for Internet of Things Applications.

Exploring Internet of Things (IoT) data streams generated by smart cities means not only transforming data into better business decisions in a timely way but also generating long-term location intelligence for developing new forms of urban governance and organization policies. This paper proposes a new architecture based on the edge-fog-cloud continuum to analyze IoT data streams for delivering data-driven insights in a smart parking scenario.

Real-Time Monitoring and Analysis of Zebrafish Electrocardiogram with Anomaly Detection.

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR), and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA) membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine.

Dry-contact microelectrode membranes for wireless detection of electrical phenotypes in neonatal mouse hearts.

Continuous monitoring of aberrant electrical rhythms during heart injury and repair requires prolonged data acquisition. We hereby developed a wearable microelectrode membrane that could be adherent to the chest of neonatal mice for in situ wireless recording of electrocardiogram (ECG) signals. The novel dry-contact membrane with a meshed parylene-C pad adjacent to the microelectrodes and the expandable meandrous strips allowed for varying size of the neonates. The performance was evaluated at the system level; specifically, the ECG signals (µV) acquired from the microelectrodes underwent two-stage amplification, band-pass filtering, and optical data transmission by an infrared Light Emitting Diode (LED) to the data-receiving unit. The circuitry was prototyped on a printed circuit board (PCB), consuming less than 300 µW, and was completely powered by an inductive coupling link. Distinct P waves, QRS complexes, and T waves of ECG signals were demonstrated from the non-pharmacologically sedated neonates at ~600 beats per minutes. Thus, we demonstrate the feasibility of both real-time and wireless monitoring cardiac rhythms in a neonatal mouse (17-20 mm and <1 g) via dry-contact microelectrode membrane; thus, providing a basis for diagnosing aberrant electrical conduction in animal models of cardiac injury and repair.

Shear stress-activated Wnt-angiopoietin-2 signaling recapitulates vascular repair in zebrafish embryos.

OBJECTIVE: Fluid shear stress intimately regulates vasculogenesis and endothelial homeostasis. The canonical Wnt/ß-catenin signaling pathways play an important role in differentiation and proliferation. In this study, we investigated whether shear stress activated angiopoietin-2 (Ang-2) via the canonical Wnt signaling pathway with an implication in vascular endothelial repair. APPROACH AND RESULTS: Oscillatory shear stress upregulated both TOPflash Wnt reporter activities and the expression of Ang-2 mRNA and protein in human aortic endothelial cells accompanied by an increase in nuclear ß-catenin intensity. Oscillatory shear stress-induced Ang-2 and Axin-2 mRNA expression was downregulated in the presence of a Wnt inhibitor, IWR-1, but was upregulated in the presence of a Wnt agonist, LiCl. Ang-2 expression was further downregulated in response to a Wnt signaling inhibitor, DKK-1, but was upregulated by Wnt agonist Wnt3a. Both DKK-1 and Ang-2 siRNA inhibited endothelial cell migration and tube formation, which were rescued by human recombinant Ang-2. Both Ang-2 and Axin-2 mRNA downregulation was recapitulated in the heat-shock-inducible transgenic Tg(hsp70l:dkk1-GFP) zebrafish embryos at 72 hours post fertilization. Ang-2 morpholino injection of Tg (kdrl:GFP) fish impaired subintestinal vessel formation at 72 hours post fertilization, which was rescued by zebrafish Ang-2 mRNA coinjection. Inhibition of Wnt signaling with IWR-1 also downregulated Ang-2 and Axin-2 expression and impaired vascular repair after tail amputation, which was rescued by zebrafish Ang-2 mRNA injection. CONCLUSIONS: Shear stress activated Ang-2 via canonical Wnt signaling in vascular endothelial cells, and Wnt-Ang-2 signaling is recapitulated in zebrafish embryos with a translational implication in vascular development and repair.

Power Approaches for Implantable Medical Devices.

Implantable medical devices have been implemented to provide treatment and to assess in vivo physiological information in humans as well as animal models for medical diagnosis and prognosis, therapeutic applications and biological science studies. The advances of micro/nanotechnology dovetailed with novel biomaterials have further enhanced biocompatibility, sensitivity, longevity and reliability in newly-emerged low-cost and compact devices. Close-loop systems with both sensing and treatment functions have also been developed to provide point-of-care and personalized medicine. Nevertheless, one of the remaining challenges is whether power can be supplied sufficiently and continuously for the operation of the entire system. This issue is becoming more and more critical to the increasing need of power for wireless communication in implanted devices towards the future healthcare infrastructure, namely mobile health (m-Health). In this review paper, methodologies to transfer and harvest energy in implantable medical devices are introduced and discussed to highlight the uses and significances of various potential power sources.

Sol-gel deposition of iridium oxide for biomedical micro-devices.

Flexible iridium oxide (IrOx)-based micro-electrodes were fabricated on flexible polyimide substrates using a sol-gel deposition process for utilization as integrated pseudo-reference electrodes for bio-electrochemical sensing applications. The fabrication method yields reliable miniature on-probe IrOx electrodes with long lifetime, high stability and repeatability. Such sensors can be used for long-term measurements. Various dimensions of sol-gel iridium oxide electrodes including 1 mm × 1 mm, 500 µm × 500 µm, and 100 µm × 100 µm were fabricated. Sensor longevity and pH dependence were investigated by immersing the electrodes in hydrochloric acid, fetal bovine serum (FBS), and sodium hydroxide solutions for 30 days. Less pH dependent responses, compared to IrOx electrodes fabricated by electrochemical deposition processes, were measured at 58.8 ± 0.4 mV/pH, 53.8 ± 1.3 mV/pH and 48 ± 0.6 mV/pH, respectively. The on-probe IrOx pseudo-reference electrodes were utilized for dopamine sensing. The baseline responses of the sensors were higher than the one using an external Ag/AgCl reference electrode. Using IrOx reference electrodes integrated on the same probe with working electrodes eliminated the use of cytotoxic Ag/AgCl reference electrode without loss in sensitivity. This enables employing such sensors in long-term recording of concentrations of neurotransmitters in central nervous systems of animals and humans.

Fetal QRS extraction from single-channel abdominal ECG using adaptive improved permutation entropy.

Fetal electrocardiogram (fECG) monitoring is crucial for assessing fetal condition during pregnancy. However, current fECG extraction algorithms are not suitable for wearable devices due to their high computational cost and multi-channel signal requirement. The paper introduces a novel and efficient algorithm called Adaptive Improved Permutation Entropy (AIPE), which can extract fetal QRS from a single-channel abdominal ECG (aECG). The proposed algorithm is robust and computationally efficient, making it a reliable and effective solution for wearable devices. To evaluate the performance of the proposed algorithm, we utilized our clinical data obtained from a pilot study with 10 subjects, each recording lasting 20 min. Additionally, data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations were simulated. The proposed methodology demonstrates an average positive predictive value ( + P ) of 91.0227%, sensitivity (Se) of 90.4726%, and F1 score of 90.6525% from the PhysioNet 2013 Challenge bank, outperforming other methods. The results suggest that AIPE could enable continuous home-based monitoring of unborn babies, even when mothers are not engaging in any hard physical activities.