Identification of gastroenteric viruses by electron microscopy using higher order spectral features

Magnetic biosensors for identification of SARS-CoV-2, Influenza, HIV, and Ebola viruses: a review

Infectious diseases such as novel coronavirus (SARS-CoV-2), Influenza, HIV, Ebola, etc kill many people around the world every year (SARS-CoV-2 in 2019, Ebola in 2013, HIV in 1980, Influenza in 1918). For example, SARS-CoV-2 has plagued higher than 317 000 000 people around the world from December 2019 to January 13, 2022. Some infectious diseases do not yet have not a proper vaccine, drug, therapeutic, and/or detection method, which makes rapid identification and definitive treatments the main challenges. Different device techniques have been used to detect infectious diseases. However, in recent years, magnetic materials have emerged as active sensors/biosensors for detecting viral, bacterial, and plasmids agents. In this review, the recent applications of magnetic materials in biosensors for infectious viruses detection have been discussed. Also, this work addresses the future trends and perspectives of magnetic biosensors.

Recent Advances in Early Diagnosis of Viruses Associated with Gastroenteritis by Biosensors

Gastroenteritis, as one of the main worldwide health challenges, especially in children, leads to 3–6 million deaths annually and causes nearly 20% of the total deaths of children aged ˂5 years, of which ~1.5 million gastroenteritis deaths occur in developing nations. Viruses are the main causative agent (~70%) of gastroenteritis episodes and their specific and early diagnosis via laboratory assays is very helpful for having successful antiviral therapy and reduction in infection burden. Regarding this importance, the present literature is the first review of updated improvements in the employing of different types of biosensors such as electrochemical, optical, and piezoelectric for sensitive, simple, cheap, rapid, and specific diagnosis of human gastroenteritis viruses. The Introduction section is a general discussion about the importance of viral gastroenteritis, types of viruses that cause gastroenteritis, and reasons for the combination of conventional diagnostic tests with biosensors for fast detection of viruses associated with gastroenteritis. Following the current laboratory detection tests for human gastroenteritis viruses and their limitations (with subsections: Electron Microscope (EM), Cell Culture, Immunoassay, and Molecular Techniques), structural features and significant aspects of various biosensing methods are discussed in the Biosensor section. In the next sections, basic information on viruses causing gastroenteritis and recent developments for fabrication and testing of different biosensors for each virus detection are covered, and the prospect of future developments in designing different biosensing platforms for gastroenteritis virus detection is discussed in the Conclusion and Future Directions section as well.

Virus identification in electron microscopy images by residual mixed attention network

BACKGROUND AND OBJECTIVE

Virus identification in electron microscopy (EM) images is considered as one of the front-line method in pathogen diagnosis and re-emerging infectious agents. However, the existing methods either focused on the detection of a single virus or required large amounts of manual labeling work to segment virus. In this work, we focus on the task of virus classification and propose an effective and simple method to identify different viruses.

METHODS

We put forward a residual mixed attention network (RMAN) for virus classification. The proposed network uses channel attention, bottom-up and top-down attention, and incorporates a residual architecture in an end-to-end training manner, which is suitable for dealing with EM virus images and reducing the burden of manual annotation.

RESULTS

We validate the proposed network through extensive experiments on a transmission electron microscopy virus image dataset. The top-1 error rate of our RMAN on 12 virus classes is 4.285%, which surpasses that of state-of-the-art networks and even human experts. In addition, the ablation study and the visualization of class activation mapping (CAM) further demonstrate the effectiveness of our method.

CONCLUSIONS

The proposed automated method contributes to the development of medical virology, which provides virologists with a high-accuracy approach to recognize viruses as well as assist in the diagnosis of viruses.

Biotechnological innovations in farm and pet animal disease diagnosis

The application of innovative diagnostic technologies for the detection of animal pathogens at an early stage is essential in restricting the economic loss incurred due to emerging infectious animal diseases. The desirable characteristics of such diagnostic methods are easy to use, cost-effective, highly sensitive, and specific, coupled with the high-throughput detection capabilities. The enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) are still the most common assays used for the detection of animal pathogens across the globe. However, utilizing the principles of ELISA and PCR, several serological and molecular technologies have been developed to achieve higher sensitivity, rapid, and point-of-care (POC) detection such as lateral flow assays, biosensors, loop-mediated isothermal amplification, recombinase polymerase amplification, and molecular platforms for field-level detection of animal pathogens. Furthermore, animal disease diagnostics need to be updated regularly to capture new, emerging and divergent infectious pathogens, and biotechnological innovations are helpful in fulfilling the rising demand for such diagnostics for the welfare of the society. Therefore, this chapter primarily describes and discusses in detail the serological, molecular, novel high-throughput, and POC assays to detect pathogens affecting farm and companion animals.

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

Rapid Viral Diagnosis of Orthopoxviruses by Electron Microscopy: Optional or a Must?

Diagnostic electron microscopy (DEM) was an essential component of viral diagnosis until the development of highly sensitive nucleic acid amplification techniques (NAT). The simple negative staining technique of DEM was applied widely to smallpox diagnosis until the world-wide eradication of the human-specific pathogen in 1980. Since then, the threat of smallpox re-emerging through laboratory escape, molecular manipulation, synthetic biology or bioterrorism has not totally disappeared and would be a major problem in an unvaccinated population. Other animal poxviruses may also emerge as human pathogens. With its rapid results (only a few minutes after arrival of the specimen), no requirement for specific reagents and its "open view", DEM remains an important component of virus diagnosis, particularly because it can easily and reliably distinguish smallpox virus or any other member of the orthopoxvirus (OPV) genus from parapoxviruses (PPV) and the far more common and less serious herpesviruses (herpes simplex and varicella zoster). Preparation, enrichment, examination, internal standards and suitable organisations are discussed to make clear its continuing value as a diagnostic technique.

Electron microscopy: essentials for viral structure, morphogenesis and rapid diagnosis

Electron microscopy (EM) should be used in the front line for detection of agents in emergencies and bioterrorism, on accounts of its speed and accuracy. However, the number of EM diagnostic laboratories has decreased considerably and an increasing number of people encounter difficulties with EM results. Therefore, the research on viral structure and morphologyant in EM diagnostic practice. EM has several technological advantages, and should be a fundamental tool in clinical diagnosis of viruses, particularly when agents are unknown or unsuspected. In this article, we review the historical contribution of EM to virology, and its use in virus differentiation, localization of specific virus antigens, virus-cell interaction, and viral morphogenesis. It is essential that EM investigations are based on clinical and comprehensive pathogenesis data from light or confocal microscopy. Furthermore, avoidance of artifacts or false results is necessary to exploit fully the advantages while minimizing its limitations.

Automatic virus particle selection--the entropy approach

This paper describes a fully automatic approach to locate icosahedral virus particles in transmission electron microscopy images. The initial detection of the particles takes place through automatic segmentation of the entropy-proportion image; this image is computed in particular regions of interest defined by two concentric structuring elements contained in a small overlapping window running over all the image. Morphological features help to select the candidates, as the threshold is kept low enough to avoid false negatives. The candidate points are subject to a credibility test based on features extracted from eight radial intensity profiles in each point from a texture image. A candidate is accepted if these features meet the set of acceptance conditions describing the typical intensity profiles of these kinds of particles. The set of points accepted is subjected to a last validation in a three-parameter space using a discrimination plan that is a function of the input image to separate possible outliers.

Application of higher order statistics/spectra in biomedical signals--a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second-order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

Modern uses of electron microscopy for detection of viruses

Electron microscopy, considered by some to be an old technique, is still on the forefront of both clinical viral diagnoses and viral ultrastructure and pathogenesis studies. In the diagnostic setting, it is particularly valuable in the surveillance of emerging diseases and potential bioterrorism viruses. In the research arena, modalities such as immunoelectron microscopy, cryo-electron microscopy, and electron tomography have demonstrated how viral structural components fit together, attach to cells, assimilate during replication, and associate with the cellular machinery during replication and egression. These studies provide information for treatment and vaccine strategies.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification.

Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.