Non-Enzymatic Glucose Sensors Composed of Polyaniline Nanofibers with High Electrochemical Performance.

The measurement of glucose concentration is a fundamental daily care for diabetes patients, and therefore, its detection with accuracy is of prime importance in the field of health care. In this study, the fabrication of an electrochemical sensor for glucose sensing was successfully designed. The electrode material was fabricated using polyaniline and systematically characterized using scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and UV-visible spectroscopy. The polyaniline nanofiber-modified electrode showed excellent detection ability for glucose with a linear range of 10 µM to 1 mM and a detection limit of 10.6 µM. The stability of the same electrode was tested for 7 days. The electrode shows high sensitivity for glucose detection in the presence of interferences. The polyaniline-modified electrode does not affect the presence of interferences and has a low detection limit. It is also cost-effective and does not require complex sample preparation steps. This makes it a potential tool for glucose detection in pharmacy and medical diagnostics.

Light-controlled growth factors release on tetrapodal ZnO-incorporated 3D-printed hydrogels for developing smart wound scaffold.

Advanced wound scaffolds that integrate active substances to treat chronic wounds have gained significant recent attention. While wound scaffolds and advanced functionalities have previously been incorporated into one medical device, the wirelessly triggered release of active substances has remained the focus of many research endeavors. To combine multiple functions including light-triggered activation, anti-septic, angiogenic, and moisturizing properties, we have developed a 3D printed hydrogel patch encapsulating vascular endothelial growth factor (VEGF) decorated with photoactive and antibacterial tetrapodal zinc oxide (t-ZnO) microparticles. To achieve the smart release of VEGF, t-ZnO was modified by chemical treatment and activated through UV/visible light exposure. This process would also make the surface rough and improve protein adhesion. The elastic modulus and degradation behavior of the composite hydrogels, which must match the wound healing process, were adjusted by changing t-ZnO concentrations. The t-ZnO-laden composite hydrogels can be printed with any desired micropattern to potentially create a modular elution of various growth factors. The VEGF decorated t-ZnO-laden hydrogel patches showed low cytotoxicity and improved angiogenic properties while maintaining antibacterial functions in vitro. In vivo tests showed promising results for the printed wound patches, with less immunogenicity and enhanced wound healing.

Optimization of physical and dielectric properties of Co-doped ZnO nanoparticles for low-frequency devices.

In this study, zinc-oxide (ZnO) nanoparticles (NPs) doped with cobalt (Co) were synthesized using a simple coprecipitation technique. The concentration of Co was varied to investigate its effect on the structural, morphological, optical, and dielectric properties of the NPs. X-ray diffraction (XRD) analysis confirmed the hexagonal wurtzite structure of both undoped and Co-doped ZnO-NPs. Scanning electron microscopy (SEM) was used to examine the morphology of the synthesized NPs, while energy-dispersive X-ray spectroscopy (EDX) was used to verify their purity. The band gap of the NPs was evaluated using UV-visible spectroscopy, which revealed a decrease in the energy gap as the concentration of Co2+ increased in the ZnO matrix. The dielectric constants and AC conductivity of the NPs were measured using an LCR meter. The dielectric constant of the Co-doped ZnO-NPs continuously increased from 4.0 × 10-9 to 2.25 × 10-8, while the dielectric loss decreased from 4.0 × 10-8 to 1.7 × 10-7 as the Co content increased from 0.01 to 0.07%. The a.c. conductivity also increased with increasing applied frequency. The findings suggest that the synthesized Co-doped ZnO-NPs possess enhanced dielectric properties and reduced energy gap, making them promising candidates for low-frequency devices such as UV photodetectors, optoelectronics, and spintronics applications. The use of a cost-effective and scalable synthesis method, coupled with detailed material characterization, makes this work significant in the field of nanomaterials and device engineering.

Electrochemical Sensing of H2O2 by Employing a Flexible Fe3O4/Graphene/Carbon Cloth as Working Electrode.

We report the synthesis of Fe3O4/graphene (Fe3O4/Gr) nanocomposite for highly selective and highly sensitive peroxide sensor application. The nanocomposites were produced by a modified co-precipitation method. Further, structural, chemical, and morphological characterization of the Fe3O4/Gr was investigated by standard characterization techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM) and high-resolution TEM (HRTEM), Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS). The average crystal size of Fe3O4 nanoparticles was calculated as 14.5 nm. Moreover, nanocomposite (Fe3O4/Gr) was employed to fabricate the flexible electrode using polymeric carbon fiber cloth or carbon cloth (pCFC or CC) as support. The electrochemical performance of as-fabricated Fe3O4/Gr/CC was evaluated toward H2O2 with excellent electrocatalytic activity. It was found that Fe3O4/Gr/CC-based electrodes show a good linear range, high sensitivity, and a low detection limit for H2O2 detection. The linear range for the optimized sensor was found to be in the range of 10-110 µM and limit of detection was calculated as 4.79 µM with a sensitivity of 0.037 µA µM-1 cm-2. The cost-effective materials used in this work as compared to noble metals provide satisfactory results. As well as showing high stability, the proposed biosensor is also highly reproducible.

Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds.

The polymerase chain reaction (PCR) test is not only time-intensive but also a contact method that puts healthcare personnel at risk. Thus, contactless and fast detection tests are more valuable. Cough sound is an important indicator of COVID-19, and in this paper, a novel explainable scheme is developed for cough sound-based COVID-19 detection. In the presented work, the cough sound is initially segmented into overlapping parts, and each segment is labeled as the input audio, which may contain other sounds. The deep Yet Another Mobile Network (YAMNet) model is considered in this work. After labeling, the segments labeled as cough are cropped and concatenated to reconstruct the pure cough sounds. Then, four fractal dimensions (FD) calculation methods are employed to acquire the FD coefficients on the cough sound with an overlapped sliding window that forms a matrix. The constructed matrixes are then used to form the fractal dimension images. Finally, a pretrained vision transformer (ViT) model is used to classify the constructed images into COVID-19, healthy and symptomatic classes. In this work, we demonstrate the performance of the ViT on cough sound-based COVID-19, and a visual explainability of the inner workings of the ViT model is shown. Three publically available cough sound datasets, namely COUGHVID, VIRUFY, and COSWARA, are used in this study. We have obtained 98.45%, 98.15%, and 97.59% accuracy for COUGHVID, VIRUFY, and COSWARA datasets, respectively. Our developed model obtained the highest performance compared to the state-of-the-art methods and is ready to be tested in real-world applications.

Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset.

Emotion identification is an essential task for human-computer interaction systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition. So far, there have been several EEG-based emotion recognition datasets that the researchers have used to validate their developed models. Hence, we have used a new ICBrainDB EEG dataset to classify angry, neutral, happy, and sad emotions in this work. Signal processing-based wavelet transform (WT), tunable Q-factor wavelet transform (TQWT), and image processing-based histogram of oriented gradients (HOG), local binary pattern (LBP), and convolutional neural network (CNN) features have been used extracted from the EEG signals. The WT is used to extract the rhythms from each channel of the EEG signal. The instantaneous frequency and spectral entropy are computed from each EEG rhythm signal. The average, and standard deviation of instantaneous frequency, and spectral entropy of each rhythm of the signal are the final feature vectors. The spectral entropy in each channel of the EEG signal after performing the TQWT is used to create the feature vectors in the second signal side method. Each EEG channel is transformed into time-frequency plots using the synchrosqueezed wavelet transform. Then, the feature vectors are constructed individually using windowed HOG and LBP features. Also, each channel of the EEG data is fed to a pretrained CNN to extract the features. In the feature selection process, the ReliefF feature selector is employed. Various feature classification algorithms namely, k-nearest neighbor (KNN), support vector machines, and neural networks are used for the automated classification of angry, neutral, happy, and sad emotions. Our developed model obtained an average accuracy of 90.7% using HOG features and a KNN classifier with a tenfold cross-validation strategy.

Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection.

BACKGROUND: The world has been suffering from the COVID-19 pandemic since 2019. More than 5 million people have died. Pneumonia is caused by the COVID-19 virus, which can be diagnosed using chest X-ray and computed tomography (CT) scans. COVID-19 also causes clinical and subclinical cardiovascular injury that may be detected on electrocardiography (ECG), which is easily accessible. METHOD: For ECG-based COVID-19 detection, we developed a novel attention-based 3D convolutional neural network (CNN) model with residual connections (RC). In this paper, the deep learning (DL) approach was developed using 12-lead ECG printouts obtained from 250 normal subjects, 250 patients with COVID-19 and 250 with abnormal heartbeat. For binary classification, the COVID-19 and normal classes were considered; and for multiclass classification, all classes. The ECGs were preprocessed into standard ECG lead segments that were channeled into 12-dimensional volumes as input to the network model. Our developed model comprised of 19 layers with three 3D convolutional, three batch normalization, three rectified linear unit, two dropouts, two additional (for residual connections), one attention, and one fully connected layer. The RC were used to improve gradient flow through the developed network, and attention layer, to connect the second residual connection to the fully connected layer through the batch normalization layer. RESULTS: A publicly available dataset was used in this work. We obtained average accuracies of 99.0% and 92.0% for binary and multiclass classifications, respectively, using ten-fold cross-validation. Our proposed model is ready to be tested with a huge ECG database.

Effects of electrically conductive nano-biomaterials on regulating cardiomyocyte behavior for cardiac repair and regeneration.

Myocardial infarction (MI) represents one of the most prevalent cardiovascular diseases, with a highly relevant and impactful role in public health. Despite the therapeutic advances of the last decades, MI still begets extensive death rates around the world. The pathophysiology of the disease correlates with cardiomyocyte necrosis, caused by an imbalance in the demand of oxygen to cardiac tissues, resulting from obstruction of the coronary flow. To alleviate the severe effects of MI, the use of various biomaterials exhibit vast potential in cardiac repair and regeneration, acting as native extracellular matrices. These hydrogels have been combined with nano sized or functional materials which possess unique electrical, mechanical, and topographical properties that play important roles in regulating phenotypes and the contractile function of cardiomyocytes even in adverse microenvironments. These nano-biomaterials' differential properties have led to substantial healing on in vivo cardiac injury models by promoting fibrotic scar reduction, hemodynamic function preservation, and benign cardiac remodeling. In this review, we discuss the interplay of the unique physical properties of electrically conductive nano-biomaterials, are able to manipulate the phenotypes and the electrophysiological behavior of cardiomyocytes in vitro, and can enhance heart regeneration in vivo. Consequently, the understanding of the decisive roles of the nano-biomaterials discussed in this review could be useful for designing novel nano-biomaterials in future research for cardiac tissue engineering and regeneration. STATEMENT OF SIGNIFICANCE: This study introduced and deciphered the understanding of the role of multimodal cues in recent advances of electrically conductive nano-biomaterials on cardiac tissue engineering. Compared with other review papers, which mainly describe these studies based on various types of electrically conductive nano-biomaterials, in this review paper we mainly discussed the interplay of the unique physical properties (electrical conductivity, mechanical properties, and topography) of electrically conductive nano-biomaterials, which would allow them to manipulate phenotypes and the electrophysiological behavior of cardiomyocytes in vitro and to enhance heart regeneration in vivo. Consequently, understanding the decisive roles of the nano-biomaterials discussed in the review could help design novel nano-biomaterials in future research for cardiac tissue engineering and regeneration.

A dual-band CPW-fed miniature planar antenna for S-, C-, WiMAX, WLAN, UWB, and X-band applications.

A miniature planar antenna is a vital component of any portable wireless communication device. The antenna in portable devices should provide wide/multiple operating bands to cover a good number of narrowband services as a multi-band antenna not only reduces the number of antennas but also lessens the system complexity, cost, and device size. To operate over S-, C-, WiMAX, WLAN, UWB, and X-communication bands, in this paper, a dual-band CPW-fed antenna is presented. The anticipated antenna is made up of a vertical bow-tie-shaped patch and two asymmetric ground planes and etched on the same side of the single-sided standard substrate material. To generate two distinct operating bands, an inverted L-shaped parasitic element is inserted within the modified U-shaped coplanar ground plane. The antenna achieved dual operating bands of 3.24-8.29 GHz and 9.12-11.25 GHz in measurement which helps the proposed antenna to cover S-, C-, WiMAX, WLAN, 4G LTE, 5G sub-6 GHz, UWB, and X-communication bands. In the two operating bands, the antenna realized a peak gain of 4.33 dBi, and 4.80 dBi, the maximum radiation efficiency of 86.6%, and 72.6%, and exhibits symmetric radiation patterns. In the operating bands, the antenna also exhibits good time-domain behavior which helps it to transmit the signal with minimum distortion.

Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals.

Autism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone to human errors, tedious, and time-consuming. Hence, a novel automated method involving the Douglas-Peucker (DP) algorithm, sparse coding-based feature mapping approach, and deep convolutional neural networks (CNNs) is employed to detect ASD using EEG recordings. Initially, the DP algorithm is used for each channel to reduce the number of samples without degradation of the EEG signal. Then, the EEG rhythms are extracted by using the wavelet transform. The EEG rhythms are coded by using the sparse representation. The matching pursuit algorithm is used for sparse coding of the EEG rhythms. The sparse coded rhythms are segmented into 8 bits length and then converted to decimal numbers. An image is formed by concatenating the histograms of the decimated rhythm signals. Extreme learning machines (ELM)-based autoencoders (AE) are employed at a data augmentation step. After data augmentation, the ASD and healthy EEG signals are classified using pre-trained deep CNN models. Our proposed method yielded an accuracy of 98.88%, the sensitivity of 100% and specificity of 96.4%, and the F1-score of 99.19% in the detection of ASD automatically. Our developed model is ready to be tested with more EEG signals before its clinical application.

Facile Fabrication of CuO Nanoparticles Embedded in N-Doped Carbon Nanostructure for Electrochemical Sensing of Dopamine.

In the present study, a highly selective and sensitive electrochemical sensing platform for the detection of dopamine was developed with CuO nanoparticles embedded in N-doped carbon nanostructure (CuO@NDC). The successfully fabricated nanostructures were characterized by standard instrumentation techniques. The fabricated CuO@NDC nanostructures were used for the development of dopamine electrochemical sensor. The reaction mechanism of a dopamine on the electrode surface is a three-electron three-proton process. The proposed sensor's performance was shown to be superior to several recently reported investigations. Under optimized conditions, the linear equation for detecting dopamine by differential pulse voltammetry is I pa (µA) = 0.07701 c (µM) - 0.1232 (R 2 = 0.996), and the linear range is 5-75 µM. The limit of detection (LOD) and sensitivity were calculated as 0.868 µM and 421.1 µA/µM, respectively. The sensor has simple preparation, low cost, high sensitivity, good stability, and good reproducibility.

Hybrid extracellular vesicles-liposome incorporated advanced bioink to deliver microRNA.

In additive manufacturing, bioink formulations govern strategies to engineer 3D living tissues that mimic the complex architectures and functions of native tissues for successful tissue regeneration. Conventional 3D-printed tissues are limited in their ability to alter the fate of laden cells. Specifically, the efficient delivery of gene expression regulators (i.e. microRNAs (miRNAs)) to cells in bioprinted tissues has remained largely elusive. In this study, we explored the inclusion of extracellular vesicles (EVs), naturally occurring nanovesicles (NVs), into bioinks to resolve this challenge. EVs show excellent biocompatibility, rapid endocytosis, and low immunogenicity, which lead to the efficient delivery of miRNAs without measurable cytotoxicity. EVs were fused with liposomes to prolong and control their release by altering their physical interaction with the bioink. Hybrid EVs-liposome (hEL) NVs were embedded in gelatin-based hydrogels to create bioinks that could efficiently encapsulate and deliver miRNAs at the target site in a controlled and sustained manner. The regulation of cells' gene expression in a 3D bioprinted matrix was achieved using the hELs-laden bioink as a precursor for excellent shape fidelity and high cell viability constructs. Novel regulatory factors-loaded bioinks will expedite the translation of new bioprinting applications in the tissue engineering field.

High-throughput and label-free multi-outlet cell counting using a single pair of impedance electrodes.

There are increasing number of cell separation applications where cells needs to be separated into multiple outlets. Quantification of sorted or separated cells and particles in microfluidic systems flowing through multiple outlet channels are typically conducted off-line through microscopic image analysis, or by first collecting cells from each outlet and counting them afterwards. However, these methods do not provide real-time analysis, are time consuming, and can lead to significant error in analysis when handling and collecting a small number of cells (such as rare cells). Here, we present a low-cost, label-free, and real-time on-chip cell counting and quantifying method for sorted/separated cells flowing through multiple microfluidic outlets using only a single pair of microelectrodes. The single staircase-shaped electrode design positioned perpendicular to the outlets subjects cells flowing through different outlets to different electric field strength, thus resulting in different impedance signals depending on which outlets the cell passes through. This design was enhanced by studying and comparing the results of both simulations and experiments. To analyze whether cells passing through each of the five outlets can be correctly classified based on their impedance peak height and width, three different classification methods were tested and compared. The developed design was successfully utilized to distinguish cells flowing through 5 different outlets using only a single pair of impedance electrodes, showing classification error rate of only 1.91%. This single-pair staircase-shaped electrode design can be applied to any cell separation system, regardless of the separation methods utilized, and thus have extremely broad application space in the field of microfluidic cell separation.