In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed ...Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiological examinations are becoming crucial in early examination of COVID-19 infection. Currently, CT findings have already been suggested as an important evidence for scientific examination of COVID-19 in Hubei, China. However, classification of patient from chest CT images is not an easy task. Therefore, in this paper, a deep bidirectional long short-term memory network with mixture density network (DBM) model is proposed. To tune the hyperparameters of the DBM model, a Memetic Adaptive Differential Evolution (MADE) algorithm is used. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) images datasets. Comparative analysis reveals that the proposed MADE-DBM model outperforms the competitive COVID-19 classification approaches in terms of various performance metrics. Therefore, the proposed MADE-DBM model can be used in real-time COVID-19 classification systems.
Electrochemical enzyme-linked immunosorbent assay (ELISA)-based immunoassays for cancer biomarker detection have recently attracted much interest owing to their higher sensitivity, amplification of ...signal, ease of handling, potential for automation and combination with miniaturized analytical systems, low cost and comparative simplicity for mass production. Their developments have considerably improved the sensitivity required for detection of low concentrations of cancer biomarkers present in bodily fluids in the early stages of the disease. Recently, various attempts have been made in their development and several methods and processes have been described for their development, amplification strategies and testing. The present review mainly focuses on the development of ELISA-based electrochemical immunosensors that may be utilized for cancer diagnosis, prognosis and therapy monitoring. Various fabrication methods and signal enhancement strategies utilized during the last few years for the development of ELISA-based electrochemical immunosensors are described.
Lipid metabolism disorders such as hypertriglyceridemia and hypercholesterolemia are risk factors for cardiovascular diseases and atherosclerosis that are grave public health issues. Taurine, a ...sulfur-containing non-essential amino acid exerts a wide range of physiological effects that regulate lipid metabolic disorders. Although the effects of taurine on lipid-lowering have been reported in animals and humans, mechanisms elucidating the lipid-lowering action of taurine remain unclear. A series of molecular regulators associated with lipid metabolism have been identified in the past few decades. These include nuclear receptors, transcription factors, and enzymes that undergo important changes during taurine treatment. In this review, we focus on the role of taurine in lipid metabolism and discuss taurine-related interventions in combating lipid disorders.
Stroke leads to inflammatory and immune response in the brain and immune organs. The gut or gastrointestinal tract is a major immune organ equipped with the largest pool of immune cells representing ...more than 70% of the entire immune system and the largest population of macrophages in the human body. The bidirectional communication between the brain and the gut is commonly known as brain-gut or gut-brain axis. Stroke often leads to gut dysmotility, gut microbiota dysbiosis, "leaky" gut, gut hemorrhage, and even gut-origin sepsis, which is often associated with poor prognosis. Emerging evidence suggests that gut inflammatory and immune response plays a key role in the pathophysiology of stroke and may become a key therapeutic target for its treatment. Ischemic brain tissue produces damage-associated molecular patterns to initiate innate and adaptive immune response both locally and systemically through the specialized pattern-recognition receptors (e.g., toll-like receptors). After stroke, innate immune cells including neutrophils, microglia or macrophages, mast cells, innate lymphocytes (IL-17 secreting γδ T-cell), and natural killer T-cell respond within hours, followed by the adaptive immune response through activation of T and B lymphocytes. Subpopulations of T-cells can help or worsen ischemic brain injury. Pro-inflammatory Th1, Th17, and γδ T-cells are often associated with increased inflammatory damage, whereas regulatory T-cells are known to suppress postischemic inflammation by increasing the secretion of anti-inflammatory cytokine IL-10. Although known to play a key role, research in the gut inflammatory and immune response after stroke is still in its initial stage. A better understanding of the gut inflammatory and immune response after stroke may be important for the development of effective stroke therapies. The present review will discuss recent advances in the studies of the brain-gut axis after stroke, the key issues to be solved, and the future directions.
This paper introduces a novel four stage filter algorithm to ameliorate images corrupted by very high density salt-and-pepper noise. The proposed algorithm exhibits two parallel trimmed median ...filters (TMF) at the initial stage followed by a masking logic that selects denoised pixel based on the priority. To reduce the blurring effect, higher priority is given to TMF with small window size. In the absence of noise-free pixels, the current extreme pixel is left unchanged at the first stage. Further, the denoising of unprocessed extreme pixels is done with TMF of large size window at the second stage. The remaining noisy pixels are improved by the running average filter at the third stage. Finally, the last stage handles the noisy pixels at the boundary and rare scenario. Since the proposed filter utilized non-extreme pixels to estimate denoinsed pixels value, it effectively eliminates salt and pepper noise along with better edge preservation. The performance analysis of the proposed filter is carried out with various benchmark images for varying noise density. The experimental results demonstrate on an average improvement of 2.09 dB (0.018) and 1.06 dB (0.0478) of PSNR (SSIM) respectively for wide (10% - 90%) and very-high (90% - 98%) noise density ranges over state-of-the-art filters.
The development of drug delivery systems using nanoparticles as carriers for small and large therapeutic molecules remains a rapidly growing area of research. The advantages of using proteins to ...prepare nanoparticles for drug delivery applications include their abundance in natural sources, biocompatibility, biodegradability, easy synthesis process, and cost-effectiveness. In contrast to several particulate systems like nanoparticles from metallic and inorganic/synthetic sources, the protein nanoparticles do not have limitations such as potential toxicity, large size, accumulation, or rapid clearance from the body. In addition, protein-based nanoparticles offer the opportunity for surface modification by conjugation of other protein and carbohydrate ligands. This enables targeted delivery to the desired tissue and organ, which further reduces systemic toxicity. The use of protein nanoparticles for such applications could therefore prove to be a better alternative to maneuver and improve the pharmacokinetic and pharmacodynamic properties of the various types of drug molecules. In this review, while focusing on the properties of a few proteins such as the silk protein fibroin, we attempt to provide an overview of the existing protein-based nanoparticles. We discuss various methods for the synthesis of this class of nanoparticles. The review brings forth some of the factors that are important for the design of this class of nanoparticles and highlights the applications of the nanoparticles obtained from these proteins.
Automatic quantification and classification of leukocytes in microscopic images are of paramount importance in the perspective of disease identification, its progress and drugs development. ...Extracting numerical values of leukocytes from microscopic images of blood or tissue sections represents a tricky challenge. Research efforts in quantification of these cells include normalization of images, segmentation of its nuclei and cytoplasm followed by their classification. However, there are several related problems viz., coarse background, overlapped nuclei, conversion of 3-D nuclei into 2-D nuclei etc. In this review, we have categorized, evaluated, and discussed recently developed methods for leukocyte identification. After reviewing these methods and finding their constraints, a future research perspective has been presented. Further, the challenges faced by the pathologists with respect to these problems are also discussed.
Image steganalysis is the process of detecting the availability of hidden messages in the cover images. Therefore, it may be considered as a classification problem which categorizes an image either ...into a cover images or a stego image. Feature selection is one of the important phases of image steganalysis which can increase its computational efficiency and performance. In this paper, a novel levy flight-based grey wolf optimization has been introduced which is used to select the prominent features for steganalysis algorithm from a set of original features. For the same, SPAM and AlexNet have been used to generate the high dimensional features. Furthermore, the random forest classifier is used to classify the images over selected features into cover images and stego images. The experimental results show that the proposed levy flight-based grey wolf optimization shows preferable convergence precision and effectively reduces the irrelevant and redundant features while maintaining the high classification accuracy as compared to other feature selection methods.
Superconducting qubits are an attractive platform for quantum computing since they have demonstrated high-fidelity quantum gates and extensibility to modest system sizes. Nonetheless, an outstanding ...challenge is stabilizing their energy-relaxation times, which can fluctuate unpredictably in frequency and time. Here, we use qubits as spectral and temporal probes of individual two-level-system defects to provide direct evidence that they are responsible for the largest fluctuations. This research lays the foundation for stabilizing qubit performance through calibration, design, and fabrication.
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