Assessing and improving the validity of COVID-19 autopsy studies - a multi center approach to establish essential standards for immunohistochemical and ultrastructural analyses

Background Autopsy studies have provided valuable insights into the pathophysiology of COVID-19. Controversies remain whether the clinical presentation is due to direct organ damage by SARS-CoV-2 or secondary effects, e.g. by an overshooting immune response. SARS-CoV-2 detection in tissues by RT-qPCR and immunohistochemistry (IHC) or electron microscopy (EM) can help answer these questions, but a comprehensive evaluation of these applications is missing. Methods We assessed publications using IHC and EM for SARS-CoV-2 detection in autopsy tissues. We systematically evaluated commercially available antibodies against the SARS-CoV-2 spike protein and nucleocapsid, dsRNA, and non-structural protein Nsp3 in cultured cell lines and COVID-19 autopsy tissues. In a multicenter study, we evaluated specificity, reproducibility, and inter-observer variability of SARS-CoV-2 nucleocapsid staining. We correlated RT-qPCR viral tissue loads with semiquantitative IHC scoring. We used qualitative and quantitative EM analyses to refine criteria for ultrastructural identification of SARS-CoV-2. Findings Publications show high variability in the detection and interpretation of SARS-CoV-2 abundance in autopsy tissues by IHC or EM. In our study, we show that IHC using antibodies against SARS-CoV-2 nucleocapsid yields the highest sensitivity and specificity. We found a positive correlation between presence of viral proteins by IHC and RT-qPCR-determined SARS-CoV-2 viral RNA load (r=-0.83, p-value <0.0001). For EM, we refined criteria for virus identification and also provide recommendations for optimized sampling and analysis. 116 of 122 publications misinterpret cellular structures as virus using EM or show only insufficient data. We provide publicly accessible digitized EM and IHC sections as a reference and for training purposes. Interpretation Since detection of SARS-CoV-2 in human autopsy tissues by IHC and EM is difficult and frequently incorrect, we propose criteria for a re-evaluation of available data and guidance for further investigations of direct organ effects by SARS-CoV-2.

Validation of a Saliva-Based Test for the Molecular Diagnosis of SARS-CoV-2 Infection

Background. Since the beginning of the pandemic, clinicians and researchers have been searching for alternative tests to improve the screening and diagnosis of the SARS-CoV-2 infection. Currently, the gold standard for virus identification is the nasopharyngeal (NP) swab. Saliva samples, however, offer clear, practical, and logistical advantages but due to a lack of collection, transport, and storage solutions, high-throughput saliva-based laboratory tests are difficult to scale up as a screening or diagnostic tool. With this study, we aimed to validate an intralaboratory molecular detection method for SARS-CoV-2 on saliva samples collected in a new storage saline solution, comparing the results to NP swabs to determine the difference in sensitivity between the two tests. Methods. In this study, 156 patients (cases) and 1005 asymptomatic subjects (controls) were enrolled and tested simultaneously for the detection of the SARS-CoV-2 viral genome by RT-PCR on both NP swab and saliva samples. Saliva samples were collected in a preservative and inhibiting saline solution (Biofarma Srl). Internal method validation was performed to standardize the entire workflow for saliva samples. Results. The identification of SARS-CoV-2 conducted on saliva samples showed a clinical sensitivity of 95.1% and specificity of 97.8% compared to NP swabs. The positive predictive value (PPV) was 81% while the negative predictive value (NPV) was 99.5%. Test concordance was 97.6% (Cohen’s Kappa = 0.86 ; 95% CI 0.81-0.91). The LoD of the test was 5 viral copies for both samples. Conclusions. RT-PCR assays conducted on a stored saliva sample achieved similar performance to those on NP swabs, and this may provide a very effective tool for population screening and diagnosis. Collection of saliva in a stabilizing solution makes the test more convenient and widely available; furthermore, the denaturing properties of the solution reduce the infective risks belonging to sample manipulation.

Detection of Cytopathic Effects Induced by Influenza, Parainfluenza, and Enterovirus Using Deep Convolution Neural Network

The isolation of a virus using cell culture to observe its cytopathic effects (CPEs) is the main method for identifying the viruses in clinical specimens. However, the observation of CPEs requires experienced inspectors and excessive time to inspect the cell morphology changes. In this study, we utilized artificial intelligence (AI) to improve the efficiency of virus identification. After some comparisons, we used ResNet-50 as a backbone with single and multi-task learning models to perform deep learning on the CPEs induced by influenza, enterovirus, and parainfluenza. The accuracies of the single and multi-task learning models were 97.78% and 98.25%, respectively. In addition, the multi-task learning model increased the accuracy of the single model from 95.79% to 97.13% when only a few data of the CPEs induced by parainfluenza were provided. We modified both models by inserting a multiplexer and de-multiplexer layer, respectively, to increase the correct rates for known cell lines. In conclusion, we provide a deep learning structure with ResNet-50 and the multi-task learning model and show an excellent performance in identifying virus-induced CPEs.

Legacy of Plant Virology in Croatia—From Virus Identification to Molecular Epidemiology, Evolution, Genomics and Beyond

This paper showcases the development of plant virology in Croatia at the University of Zagreb, Faculty of Science, from its beginning in the 1950s until today, more than 70 years later. The main achievements of the previous and current group members are highlighted according to various research topics and fields. Expectedly, some of those accomplishments remained within the field of plant virology, but others make part of a much-extended research spectrum exploring subviral pathogens, prokaryotic plant pathogens, fungi and their viruses, as well as their interactions within ecosystems. Thus, the legacy of plant virology in Croatia continues to contribute to the state of the art of microbiology far beyond virology. Research problems pertinent for directing the future research endeavors are also proposed in this review.

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

A Retrospective Paired Comparison Between Untargeted Next Generation Sequencing and Conventional Microbiology Tests With Wisely Chosen Metagenomic Sequencing Positive Criteria

Objectives: To evaluate the performance of metagenomic next generation sequencing (mNGS) using adequate criteria for the detection of pathogens in lower respiratory tract (LRT) samples with a paired comparison to conventional microbiology tests (CMT).Methods: One hundred sixty-seven patients were reviewed from four different intensive care units (ICUs) in mainland China during 2018 with both mNGS and CMT results of LRT samples available. The reads per million ratio (RPMsample/RPMnon−template−control ratio) and standardized strictly mapped reads number (SDSMRN) were the two criteria chosen for identifying positive pathogens reported from mNGS. A McNemar test was used for a paired comparison analysis between mNGS and CMT.Results: One hundred forty-nine cases were counted into the final analysis. The RPMsample/RPMNTC ratio criterion performed better with a higher accuracy for bacteria, fungi, and virus than SDSMRN criterion [bacteria (RPMsample/RPMNTC ratio vs. SDSMRN), 65.1 vs. 55.7%; fungi, 75.8 vs. 71.1%; DNA virus, 86.3 vs. 74.5%; RNA virus, 90.9 vs. 81.8%]. The mNGS was also superior in bacteria detection only if an SDSMRN ≥3 was used as a positive criterion with a paired comparison to culture [SDSMRN positive, 92/149 (61.7%); culture positive, 54/149 (36.2%); p &lt; 0.001]; however, it was outperformed with significantly more fungi and DNA virus identification when choosing both criteria for positive outliers [fungi (RPMsample/RPMNTC ratio vs. SDSMRN vs. culture), 23.5 vs. 29.5 vs. 8.7%, p &lt; 0.001; DNA virus (RPMsample/RPMNTC ratio vs. SDSMRN vs. PCR), 14.1 vs. 20.8 vs. 11.8%, p &lt; 0.05].Conclusions: Metagenomic next generation sequencing may contribute to revealing the LRT infection etiology in hospitalized groups of potential fungal infections and in situations with less access to the multiplex PCR of LRT samples from the laboratory by choosing a wise criterion like the RPMsample/RPMNTC ratio.

VALIDATION OF A SALIVA-BASED TEST FOR THE MOLECULAR DIAGNOSIS OF SARS-CoV-2 INFECTION

Background: Since the beginning of the pandemic, clinicians and researchers have been searching for alternative tests to improve screening and diagnosis of SARS-CoV-2 infection. Currently, the gold standard for virus identification is the nasopharyngeal (NP) swab. Saliva samples, however, offer clear practical and logistical advantages but due to lack of collection, transport, and storage solutions, high-throughput saliva-based laboratory tests are difficult to scale up as a screening or diagnostic tool. With this study, we aimed to validate an intra-laboratory molecular detection method for SARS-CoV-2 on saliva samples collected in a new storage and inactivating solution, comparing the results to NP swabs to determine the difference in sensitivity between the two tests. Methods: In this study, 156 patients (cases) and 1005 asymptomatic subjects (controls) were enrolled and tested simultaneously for the detection of the SARS-CoV-2 viral genome by RT-PCR on both NP swab and saliva samples. Saliva samples were collected in a preservative and inhibiting saline solution (Biofarma Srl). Internal method validation was performed to standardize the entire workflow for saliva samples. Results: The identification of SARS-CoV-2 conducted on saliva samples showed a clinical sensitivity of 95.1% and specificity of 97.8% compared to NP swabs. The positive predictive value (PPV) was 81% while the negative predictive value (NPV) was 99.5%. Test concordance was 97.6% (Cohen's Kappa=0.86; 95% CI 0.81-0.91). The LoD of the test was 5 viral copies for both samples. Conclusions: RT-PCR assays conducted on a stored saliva sample achieved similar performance to those on NP swabs and this may provide a very effective tool for population screening and diagnosis. Collection of saliva in a stabilizing solution makes the test more convenient and widely available; furthermore, the denaturation properties of the solution reduces the infective risks belonging to sample manipulation.

High-throughput identification of viral termini and packaging mechanisms in virome datasets using PhageTermVirome

Viruses that infect bacteria (phages) are increasingly recognized for their importance in diverse ecosystems but identifying and annotating them in large-scale sequence datasets is still challenging. Although efficient scalable virus identification tools are emerging, defining the exact ends (termini) of phage genomes is still particularly difficult. The proper identification of termini is crucial, as it helps in characterizing the packaging mechanism of bacteriophages and provides information on various aspects of phage biology. Here, we introduce PhageTermVirome (PTV) as a tool for the easy and rapid high-throughput determination of phage termini and packaging mechanisms using modern large-scale metagenomics datasets. We successfully tested the PTV algorithm on a mock virome dataset and then used it on two real virome datasets to achieve the rapid identification of more than 100 phage termini and packaging mechanisms, with just a few hours of computing time. Because PTV allows the identification of free fully formed viral particles (by recognition of termini present only in encapsidated DNA), it can also complement other virus identification softwares to predict the true viral origin of contigs in viral metagenomics datasets. PTV is a novel and unique tool for high-throughput characterization of phage genomes, including phage termini identification and characterization of genome packaging mechanisms. This software should help researchers better visualize, map and study the virosphere. PTV is freely available for downloading and installation at https://gitlab.pasteur.fr/vlegrand/ptv.

BEMD-3DCNN-Based Method for COVID-19 Detection

Coronavirus outbreak continues to spread around the world and none knows when it will stop. Therefore, from the first day of the virus identification in Wuhan, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the right medicine to help and protect patients. A fast diagnostic and detection system is a priority and should be found to stop COVID-19 from spreading. Medical imaging techniques has been used for this purpose. The existing works used transfer learning by exploiting different backbones like VGG, ResNet, DenseNet or combine them to detect COVID-19. By using these backbones many aspect can not be analysed like the spatial and contextual information in the images, while these information's can be useful for a better detection performance. For that in this paper, we used 3D representation of the data (video) as input of the 3DCNN-based deep learning model. The Bi-dimensional empirical mode decomposition (BEMD) technique to decompose the original image into IMFs, then built a video of these IMFs images. The formed video is used as input of 3DCNN model to classify and detect COVID-19 virus. 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) modules then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows a learning from different feature maps. In the experiments we used 6484 X-Ray images, 1802 of them were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed techniques achieved the desired results on the selected dataset. Also, the use of 3DCNN model with contextual information processing exploiting CAA networks helps to achieve better performance.

Geography and covid-19: reflections from the evolution of the pandemic in Uberlândia, Brazil

From the complex sanitary moment faced by Brazil, related to COVID-19, in particular due to the collapse of the health system that has occurred in medium-sized cities, this article aimed to analyze the evolution of the COVID-19 pandemic in Uberlândia, according to reflections from geographic science, in order to help to understand the facts that led to the current scenario of the disease in the city. To achieve the objective, we carried out a theoretical discussion about how Geography faces recent events, especially in relation to the role of the Globalization process, both in the dissemination of information about the emergence of COVID-19 and other events, as well as in the rapid spread of the virus around the world. Based on these reflections, we analyzed the evolution of the pandemic, using data provided by the Municipality of Uberlândia and the Ministry of Health, with a cut-off date of March 18, 2021, in order to understand the chronological path of the facts. The choice for this cut-off date for data clipping is justified by our intention to analyze the first year of virus identification in the municipality. The results showed the negative evolution of the pandemic in the city, mainly over the months of February and March 2021, with 100% of the ICU beds occupied, which demonstrates the complexity and the long way to go to overcome this health crisis.