Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus

SPM4GAC: SPM based approach for genome analysis and classification of macromolecules

Genome sequence analysis and classification play critical roles in properly understanding an organism's main characteristics, functionalities, and changing (evolving) nature. However, the rapid expansion of genomic data makes genome sequence analysis and classification a challenging task due to the high computational requirements, proper management, and understanding of genomic data. Recently proposed models yielded promising results for the task of genome sequence classification. Nevertheless, these models often ignore the sequential nature of nucleotides, which is crucial for revealing their underlying structure and function. To address this limitation, we present SPM4GAC, a sequential pattern mining (SPM)-based framework to analyze and classify the macromolecule genome sequences of viruses. First, a large dataset containing the genome sequences of various RNA viruses is developed and transformed into a suitable format. On the transformed dataset, algorithms for SPM are used to identify frequent sequential patterns of nucleotide bases. The obtained frequent sequential patterns of bases are then used as features to classify different viruses. Ten classifiers are employed, and their performance is assessed by using several evaluation measures. Finally, a performance comparison of SPM4GAC with state-of-the-art methods for genome sequence classification/detection reveals that SPM4GAC performs better than those methods.

A systematic review and meta-analysis on the prevalence and impact of coronary artery disease in hospitalized COVID-19 patients

Background

COVID-19 accounts for more than half a billion deaths globally. The clinical manifestations may vary in due course. Despite several studies aimed at determining the extent to which the disease's severity and mortality remain high when combined with other comorbidities, more research is required. Therefore, this review aimed to measure the pooled prevalence of coronary artery disease (CAD) among COVID-19 patients, specifically those with a history of CAD. Additionally, we aim to assess the association between mortality due to CAD and the severity of COVID-19 among hospitalized patients.

Method

A comprehensive search in PubMed, Web of Science, the Cochrane Library, and the WHO COVID-19 database was conducted. English studies with original data on CAD, mortality, and ARDS among COVID-19 patients were included. PRISMA guidelines were followed.

Results

Among the 2007 identified articles, 76 studies met the inclusion criteria. The pooled prevalence of CAD among COVID-19 patients was 14.4%(95% CI: 12.7-16.2). The highest prevalence was observed in European studies at 18.2%(95% CI: 13.3-24.2), while the lowest was in Asian studies at 10.4% (95% CI: 6.4-16.3). Participants with concurrent CAD at the time of hospital admission had twice the odds of mortality due to COVID-19 (2.64 [95% CI: 2.30-3.04]) with moderate heterogeneity (I2 = 45%, p < 0.01). Hospitalized COVID-19 patients with CAD had a 50% higher risk of ARDS (95% CI: 0.62-3.66), but this difference was not statistically significant.

Conclusion

Although our analysis revealed evidence for a relationship between concurrent CAD at the time of hospital admission and mortality from COVID-19, however, global variation in health infrastructure, limitations of data reporting, and the effects of emerging variants must be considered in future investigations.

SARS-CoV-2 spike gene Sanger sequencing methodology to identify variants of concern

The global demand for rapid identification of circulating SARS-CoV-2 variants of concern has led to a shortage of commercial kits. Therefore, this study aimed to develop and validate a rapid, cost-efficient genome sequencing protocol to identify circulating SARS-CoV-2 (variants of concern). Sets of primers flanking the SARS-CoV-2 spike gene were designed, verified and then validated using 282 nasopharyngeal positive samples for SARS-CoV-2. Protocol specificity was confirmed by comparing these results with SARS-CoV-2 whole-genome sequencing of the same samples. Out of 282 samples, 123 contained the alpha variant, 78 beta and 13 delta, which were indicted using in-house primers and next-generation sequencing; the numbers of variants found were 100% identical to the reference genome. This protocol is easily adaptable for detection of emerging variants during the pandemic.

COVID-19 (novel SARS-CoV-2) neurological illness

COVID-19 illness is associated with diverse neurological manifestations. Its exceptionally high prevalence results from unprecedented genetic diversity, genomic recombination, and superspreading. With each new mutation and variant, there are foreseeable risks of rising fatality and novel neurological motor complications in childhood and adult cases. This chapter provides an extensive review of COVID-19 neurological illness, notably the motor manifestations. Innovative treatments have been developed to stem the spread of infectious contagious illness, and attenuate the resultant cytokine storm and other postinfectious immune aspects responsible for postacute COVID-19 syndrome due to the multiplier effect of infection, immunity, and inflammation, termed I3.

Classifying COVID-19 based on amino acids encoding with machine learning algorithms

COVID-19 disease causes serious respiratory illnesses. Therefore, accurate identification of the viral infection cycle plays a key role in designing appropriate vaccines. The risk of this disease depends on proteins that interact with human receptors. In this paper, we formulate a novel model for COVID-19 named "amino acid encoding based prediction" (AAPred). This model is accurate, classifies the various coronavirus types, and distinguishes SARS-CoV-2 from other coronaviruses. With the AAPred model, we reduce the number of features to enhance its performance by selecting the most important ones employing statistical criteria. The protein sequence of SARS-CoV-2 for understanding the viral infection cycle is analyzed. Six machine learning classifiers related to decision trees, k-nearest neighbors, random forest, support vector machine, bagging ensemble, and gradient boosting are used to evaluate the model in terms of accuracy, precision, sensitivity, and specificity. We implement the obtained results computationally and apply them to real data from the National Genomics Data Center. The experimental results report that the AAPred model reduces the features to seven of them. The average accuracy of the 10-fold cross-validation is 98.69%, precision is 98.72%, sensitivity is 96.81%, and specificity is 97.72%. The features are selected utilizing information gain and classified with random forest. The proposed model predicts the type of Coronavirus and reduces the number of extracted features. We identify that SARS-CoV-2 has similar physicochemical characteristics in some regions of SARS-CoV. Also, we report that SARS-CoV-2 has similar infection cycles and sequences in some regions of SARS CoV indicating the affectedness of vaccines on SARS-CoV-2. A comparison with deep learning shows similar results with our method.

SARS-CoV-2 and Emerging Variants: Unmasking Structure, Function, Infection, and Immune Escape Mechanisms

As of April 1, 2022, over 468 million COVID-19 cases and over 6 million deaths have been confirmed globally. Unlike the common coronavirus, SARS-CoV-2 has highly contagious and attracted a high level of concern worldwide. Through the analysis of SARS-CoV-2 structural, non-structural, and accessory proteins, we can gain a deeper understanding of structure-function relationships, viral infection mechanisms, and viable strategies for antiviral therapy. Angiotensin-converting enzyme 2 (ACE2) is the first widely acknowledged SARS-CoV-2 receptor, but researches have shown that there are additional co-receptors that can facilitate the entry of SARS-CoV-2 to infect humans. We have performed an in-depth review of published papers, searching for co-receptors or other auxiliary membrane proteins that enhance viral infection, and analyzing pertinent pathogenic mechanisms. The genome, and especially the spike gene, undergoes mutations at an abnormally high frequency during virus replication and/or when it is transmitted from one individual to another. We summarized the main mutant strains currently circulating global, and elaborated the structural feature for increased infectivity and immune evasion of variants. Meanwhile, the principal purpose of the review is to update information on the COVID-19 outbreak. Many countries have novel findings on the early stage of the epidemic, and accruing evidence has rewritten the timeline of the outbreak, triggering new thinking about the origin and spread of COVID-19. It is anticipated that this can provide further insights for future research and global epidemic prevention and control.

Using amino acid features to identify the pathogenicity of influenza B virus

BACKGROUND

Influenza B virus can cause epidemics with high pathogenicity, so it poses a serious threat to public health. A feature representation algorithm is proposed in this paper to identify the pathogenicity phenotype of influenza B virus.

METHODS

The dataset included all 11 influenza virus proteins encoded in eight genome segments of 1724 strains. Two types of features were hierarchically used to build the prediction model. Amino acid features were directly delivered from 67 feature descriptors and input into the random forest classifier to output informative features about the class label and probabilistic prediction. The sequential forward search strategy was used to optimize the informative features. The final features for each strain had low dimensions and included knowledge from different perspectives, which were used to build the machine learning model for pathogenicity identification.

RESULTS

The 40 signature positions were achieved by entropy screening. Mutations at position 135 of the hemagglutinin protein had the highest entropy value (1.06). After the informative features were directly generated from the 67 random forest models, the dimensions for class and probabilistic features were optimized as 4 and 3, respectively. The optimal class features had a maximum accuracy of 94.2% and a maximum Matthews correlation coefficient of 88.4%, while the optimal probabilistic features had a maximum accuracy of 94.1% and a maximum Matthews correlation coefficient of 88.2%. The optimized features outperformed the original informative features and amino acid features from individual descriptors. The sequential forward search strategy had better performance than the classical ensemble method.

CONCLUSIONS

The optimized informative features had the best performance and were used to build a predictive model so as to identify the phenotype of influenza B virus with high pathogenicity and provide early risk warning for disease control.

Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms

The coronavirus 3C-like (3CL) protease, a cysteine protease, plays an important role in viral infection and immune escape. However, there is still a lack of effective tools for determining the cleavage sites of the 3CL protease. This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein, and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus, Betacoronavirus and Gammacoronavirus. Strong residue preferences were observed at the neighboring positions of the cleavage sites. A random forest (RF) model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes, and the model achieved an AUC of 0.96 in cross-validations. The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts. It achieved an AUC of 0.95 and predicted correctly 80% of the cleavage sites. Then, 1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model. These proteins were enriched in several GO terms related to the cytoskeleton, such as the microtubule, actin and tubulin. Finally, a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model. Overall, the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.

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A view of the diversity of the cleavage sites by the coronavirus 3CL protease.

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An accurate RF model for predicting the cleavage of coronavirus 3CL protease.

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A web-server named 3CLP for predicting the cleavage of coronavirus 3CL protease.

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The 3CLP is available at http://www.computationalbiology.cn/3CLPHost/home.html.

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1352 human proteins were predicted to be cleaved by the coronavirus 3CL protease.

Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2

Knowledge of the host-specific properties of the spike protein is of crucial importance to understand the adaptability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to infect multiple species and alter transmissibility, particularly in humans. Here, we propose a spike protein predictor SPIKES incorporating with an inheritable bi-objective combinatorial genetic algorithm to identify the biochemical properties of spike proteins and determine their specificity to human hosts. SPIKES identified 20 informative physicochemical properties of the spike protein, including information measures for alpha helix and relative mutability, and amino acid and dipeptide compositions, which have shown compositional difference at the amino acid sequence level between human and diverse animal coronaviruses. We suggest that alterations of these amino acids between human and animal coronaviruses may provide insights into the development and transmission of SARS-CoV-2 in human and other species and support the discovery of targeted antiviral therapies.

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Differences exist in the amino acids within the S protein of diverse host species CoVs

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We developed SPIKES to identify informative properties of S protein

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SARS-CoV-2 variants have amino acid changes that alter infection and transmission

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The SPIKES identified changes in S protein properties from animal to human host CoVs

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

A Global Mutational Profile of SARS-CoV-2: A Systematic Review and Meta-Analysis of 368,316 COVID-19 Patients

Since its first detection in December 2019, more than 232 million cases of COVID-19, including 4.7 million deaths, have been reported by the WHO. The SARS-CoV-2 viral genomes have evolved rapidly worldwide, causing the emergence of new variants. This systematic review and meta-analysis was conducted to provide a global mutational profile of SARS-CoV-2 from December 2019 to October 2020. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA), and a study protocol was lodged with PROSPERO. Data from 62 eligible studies involving 368,316 SARS-CoV-2 genomes were analyzed. The mutational data analyzed showed most studies detected mutations in the Spike protein (n = 50), Nucleocapsid phosphoprotein (n = 34), ORF1ab gene (n = 29), 5′-UTR (n = 28) and ORF3a (n = 25). Under the random-effects model, pooled prevalence of SARS-CoV-2 variants was estimated at 95.1% (95% CI; 93.3–96.4%; I² = 98.952%; p = 0.000) while subgroup meta-analysis by country showed majority of the studies were conducted ‘Worldwide’ (n = 10), followed by ‘Multiple countries’ (n = 6) and the USA (n = 5). The estimated prevalence indicated a need to continuously monitor the prevalence of new mutations due to their potential influence on disease severity, transmissibility and vaccine effectiveness.

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

Background

Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes.

Methods

A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models.

Results

The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor.

Conclusions

Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-021-00912-6.

The impact of the hypoxia-VEGF-vascular permeability on COVID-19-infected patients

Effective treatment of patients with severe COVID-19 to reduce mortality remains one of the most challenging medical issues in controlling unpredictable emergencies caused by the global pandemics. Unfortunately, such effective therapies are not available at this time of writing. In this article, I discuss the possibility of repurposing clinically available anti-VEGF (vascular endothelial growth factor) drugs that are routinely used in oncology and ophthalmology areas for effective treatment of patients with severe and critical COVID-19. Our preliminary findings from a clinical trial support the therapeutic concept of using anti-VEGF for treating patients with severe COVID-19 to reduce mortality. The aim of this article is to further provide mechanistic insights into the role of VEGF in causing pathological changes during COVID-19 infection.

Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.

Comparative Genomics Reveals Early Emergence and Biased Spatiotemporal Distribution of SARS-CoV-2

Effective systems for the analysis of molecular data are fundamental for monitoring the spread of infectious diseases and studying pathogen evolution. The rapid identification of emerging viral strains, and/or genetic variants potentially associated with novel phenotypic features is one of the most important objectives of genomic surveillance of human pathogens and represents one of the first lines of defense for the control of their spread. During the COVID 19 pandemic, several taxonomic frameworks have been proposed for the classification of SARS-Cov-2 isolates. These systems, which are typically based on phylogenetic approaches, represent essential tools for epidemiological studies as well as contributing to the study of the origin of the outbreak. Here, we propose an alternative, reproducible, and transparent phenetic method to study changes in SARS-CoV-2 genomic diversity over time. We suggest that our approach can complement other systems and facilitate the identification of biologically relevant variants in the viral genome. To demonstrate the validity of our approach, we present comparative genomic analyses of more than 175,000 genomes. Our method delineates 22 distinct SARS-CoV-2 haplogroups, which, based on the distribution of high-frequency genetic variants, fall into four major macrohaplogroups. We highlight biased spatiotemporal distributions of SARS-CoV-2 genetic profiles and show that seven of the 22 haplogroups (and of all of the four haplogroup clusters) showed a broad geographic distribution within China by the time the outbreak was widely recognized—suggesting early emergence and widespread cryptic circulation of the virus well before its isolation in January 2020. General patterns of genomic variability are remarkably similar within all major SARS-CoV-2 haplogroups, with UTRs consistently exhibiting the greatest variability, with s2m, a conserved secondary structure element of unknown function in the 3′-UTR of the viral genome showing evidence of a functional shift. Although several polymorphic sites that are specific to one or more haplogroups were predicted to be under positive or negative selection, overall our analyses suggest that the emergence of novel types is unlikely to be driven by convergent evolution and independent fixation of advantageous substitutions, or by selection of recombined strains. In the absence of extensive clinical metadata for most available genome sequences, and in the context of extensive geographic and temporal biases in the sampling, many questions regarding the evolution and clinical characteristics of SARS-CoV-2 isolates remain open. However, our data indicate that the approach outlined here can be usefully employed in the identification of candidate SARS-CoV-2 genetic variants of clinical and epidemiological importance.

Distinct Mucoinflammatory Phenotype and the Immunomodulatory Long Noncoding Transcripts Associated with SARS-CoV-2 Airway Infection

Respiratory epithelial cells are the primary target for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We investigated the 3D human airway tissue model to evaluate innate epithelial cell responses to SARS-CoV-2 infection. A SARS-CoV-2 clinical isolate productively infected the 3D-airway model with a time-dependent increase in viral load (VL) and concurrent upregulation of airway immunomodulatory factors ( IL-6, ICAM-1 , and SCGB1A1 ) and respiratory mucins ( MUC5AC, MUC5B, MUC2 , and MUC4) , and differential modulation of select long noncoding RNAs (lncRNAs i.e., LASI, TOSL, NEAT1 , and MALAT1 ). Next, we examined these immunomodulators in the COVID-19 patient nasopharyngeal swab samples collected from subjects with high- or low-VLs (∼100-fold difference). As compared to low-VL, high-VL patients had prominent mucoinflammatory signature with elevated expression of IL-6, ICAM-1, SCGB1A1, SPDEF, MUC5AC, MUC5B , and MUC4 . Interestingly, LASI, TOSL , and NEAT1 lncRNA expressions were also markedly elevated in high-VL patients with no change in MALAT1 expression. In addition, dual-staining of LASI and SARS-CoV-2 nucleocapsid N1 RNA showed predominantly nuclear/perinuclear localization at 24 hpi in 3D-airway model as well as in high-VL COVID-19 patient nasopharyngeal cells, which exhibited high MUC5AC immunopositivity. Collectively, these findings suggest SARS-CoV-2 induced lncRNAs may play a role in acute mucoinflammatory response observed in symptomatic COVID-19 patients.

Using data mining techniques to fight and control epidemics: A scoping review

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles

Objective

This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer questions related to dataset contents, accessibility and citations.

Methods

We downloaded COVID-19-related full-text articles published until 31 May 2020 from PubMed Central. Dataset URL links mentioned in full-text articles were extracted, and each dataset was manually reviewed to provide information on 10 variables: (1) type of the dataset, (2) geographic region where the data were collected, (3) whether the dataset was immediately downloadable, (4) format of the dataset files, (5) where the dataset was hosted, (6) whether the dataset was updated regularly, (7) the type of license used, (8) whether the metadata were explicitly provided, (9) whether there was a PubMed Central paper describing the dataset and (10) the number of times the dataset was cited by PubMed Central articles. Descriptive statistics about these seven variables were reported for all extracted datasets.

Results

We found that 28.5% of 12 324 COVID-19 full-text articles in PubMed Central provided at least one dataset link. In total, 128 unique dataset links were mentioned in 12 324 COVID-19 full text articles in PubMed Central. Further analysis showed that epidemiological datasets accounted for the largest portion (53.9%) in the dataset collection, and most datasets (84.4%) were available for immediate download. GitHub was the most popular repository for hosting COVID-19 datasets. CSV, XLSX and JSON were the most popular data formats. Additionally, citation patterns of COVID-19 datasets varied depending on specific datasets.

Conclusion

PubMed Central articles are an important source of COVID-19 datasets, but there is significant heterogeneity in the way these datasets are mentioned, shared, updated and cited.

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review

BACKGROUND

COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has become a global pandemic, affecting most countries worldwide. Digital health information technologies can be applied in three aspects, namely digital patients, digital devices, and digital clinics, and could be useful in fighting the COVID-19 pandemic.

OBJECTIVE

Recent reviews have examined the role of digital health in controlling COVID-19 to identify the potential of digital health interventions to fight the disease. However, this study aims to review and analyze the digital technology that is being applied to control the COVID-19 pandemic in the 10 countries with the highest prevalence of the disease.

METHODS

For this review, the Google Scholar, PubMed, Web of Science, and Scopus databases were searched in August 2020 to retrieve publications from December 2019 to March 15, 2020. Furthermore, the Google search engine was used to identify additional applications of digital health for COVID-19 pandemic control.

RESULTS

We included 32 papers in this review that reported 37 digital health applications for COVID-19 control. The most common digital health projects to address COVID-19 were telemedicine visits (11/37, 30%). Digital learning packages for informing people about the disease, geographic information systems and quick response code applications for real-time case tracking, and cloud- or mobile-based systems for self-care and patient tracking were in the second rank of digital tool applications (all 7/37, 19%). The projects were deployed in various European countries and in the United States, Australia, and China.

CONCLUSIONS

Considering the potential of available information technologies worldwide in the 21st century, particularly in developed countries, it appears that more digital health products with a higher level of intelligence capability remain to be applied for the management of pandemics and health-related crises.

Asymptomatic SARS-CoV-2 Carriers: A Systematic Review and Meta-Analysis

Asymptomatic cases of SARS-CoV-2 can be unknown carriers magnifying the transmission of COVID-19. This study appraised the frequency of asymptomatic individuals and estimated occurrence by age group and gender by reviewing the existing published data on asymptomatic people with COVID-19. Three electronic databases, PubMed, Embase, and Web of Science (WoS), were used to search the literature following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The study population for this review included asymptomatic individuals infected with SARS-CoV-2 reported in original articles published up to 30 April 2020. A random effects model was applied to analyze pooled data on the prevalence of asymptomatic cases among all COVID-19 patients and also by age and gender. From the meta-analysis of 16 studies, comprising 2,788 SARS-CoV-2 infected patients, the pooled prevalence according to the random effect size of asymptomatic cases was 48.2% (95% CI, 30-67%). Of the asymptomatic cases, 55.5% (95% CI, 43.6-66.8%) were female and 49.6% (95% CI, 20.5-79.1%) were children. Children and females were more likely to present as asymptomatic COVID-19 cases and could act as unknown carriers of SARS-CoV-2. Symptom-based screening might fail to identify all SARS-CoV-2 infections escalating the threat of global spread and impeding containment. Therefore, a mass surveillance system to track asymptomatic cases is critical, with special attention to females and children.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

BACKGROUND

SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

OBJECTIVE

The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

METHODS

A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.

RESULTS

The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.

CONCLUSIONS

In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Full length genomic sanger sequencing and phylogenetic analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Nigeria

In an outbreak, effective detection of the aetiological agent(s) involved using molecular techniques is key to efficient diagnosis, early prevention and management of the spread. However, sequencing is necessary for mutation monitoring and tracking of clusters of transmission, development of diagnostics and for vaccines and drug development. Many sequencing methods are fast evolving to reduce test turn-around-time and to increase through-put compared to Sanger sequencing method; however, Sanger sequencing remains the gold standard for clinical research sequencing with its 99.99% accuracy This study sought to generate sequence data of SARS-CoV-2 using Sanger sequencing method and to characterize them for possible site(s) of mutations. About 30 pairs of primers were designed, synthesized, and optimized using endpoint PCR to generate amplicons for the full length of the virus. Cycle sequencing using BigDye Terminator v.3.1 and capillary gel electrophoresis on ABI 3130xl genetic analyser were performed according to the manufacturers’ instructions. The sequence data generated were assembled and analysed for variations using DNASTAR Lasergene 17 SeqMan Ultra. Total length of 29,760bp of SARS-CoV-2 was assembled from the sample analysed and deposited in GenBank with accession number: MT576584. Blast result of the sequence assembly shows a 99.97% identity with the reference sequence. Variations were noticed at positions: nt201, nt2997, nt14368, nt16535, nt20334, and nt28841-28843, which caused amino acid alterations at the S (aa614) and N (aa203-204) regions. The mutations observed at S and N-gene in this study may be indicative of a gradual changes in the genetic coding of the virus hence, the need for active surveillance of the viral genome.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review

BACKGROUND

In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.

OBJECTIVE

This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.

METHODS

A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.

RESULTS

We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.

CONCLUSIONS

The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone

Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F₁ scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity.

Coronavirus 2019: clinical and neuropathological aspects

PURPOSE OF REVIEW

To understand the role of postinfectious autoimmune vascular inflammation in the pathogenesis of coronavirus disease 2019-related neurological illness caused by the novel severe acute respiratory syndrome coronavirus 2 virus and its effects on the brain in children and adults.

RECENT FINDINGS

There are a very small number of postmortem neuropathological series of coronavirus disease 2019-related cerebrovascular and parenchymal disease. However, they fall into at least three major categories, with the majority manifesting those of terminal hypoxia, and others demonstrating inflammatory vascular leptomeningeal, cerebral and brainstem interstitial changes suspicious for encephalitis in a minority of cases. It remains uncertain whether these histopathological features have a relationship to post-infectious inflammatory immune mechanisms and microscopic vasculitis in adults as it appears to be in affected children with multisystem inflammatory syndrome.

SUMMARY

The reasons for this dichotomy are unclear but may related to inherent and epigenetic factors that remain poorly understood. Treatment addressing postinfectious mechanisms of pulmonary, systemic, and nervous system injury may avert early mortality.

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

BACKGROUND AND OBJECTIVE

The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis.

METHODS

We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.

RESULTS

The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.

CONCLUSIONS

The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.

Coronavirus (SARS-CoV-2) Pandemic: Future Challenges for Dental Practitioners

In the context of the SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) pandemic, the medical system has been subjected to many changes. Face-to-face treatments have been suspended for a period of time. After the lockdown, dentists have to be aware of the modalities to protect themselves and their patients in order not to get infected. Dental practitioners are potentially exposed to a high degree of contamination with SARS-CoV-2 while performing dental procedures that produce aerosols. It should also be noted that the airways, namely the oral cavity and nostrils, are the access pathways for SARS-CoV-2. In order to protect themselves and their patients, they have to use full personal protective equipment. Relevant data regarding this pandemic are under evaluation and are still under test. In this article, we made a synthesis about the way in which SARS-CoV-2 spreads, how to diagnose a novel corona virus infection, what the possible treatments are, and which protective personal equipment we can use to stop its spreading.

SARS-CoV-2 and cancer: Are they really partners in crime?

The outbreak of the SARS-CoV-2 pandemic has overwhelmed health care systems in many countries. The clinical presentation of the SARS-CoV-2 varies between a subclinical or flu-like syndrome to that of severe pneumonia with multi-organ failure and death. Initial reports have suggested that cancer patients may have a higher susceptibility to get infected by the SARS-CoV-2 virus but current evidence remains poor as it is biased by important confounders. Patients with ongoing or recent cancer treatment for advanced active disease, metastatic solid tumors and hematological malignancies are at higher risk of developing severe COVID-19 respiratory disease that requires hospitalization and have a poorer disease outcome compared to individuals without cancer. However it is not clear whether these are independent risk factors, or mainly driven by male gender, age, obesity, performance status, uncontrolled diabetes, cardiovascular disease and various other medical conditions. These often have a greater influence on the probability to die due to SARS-CoV-2 then cancer. Delayed diagnosis and suboptimal cancer management due to the pandemic results in disease upstaging and has considerable impact cancer on specific death rates. Surgery during the peak of the pandemic seems to increase mortality, but there is no convincing evidence that adjuvant systemic cancer therapy and radiotherapy are contraindicated, implicating that cancer treatment can be provided safely after individual risk/benefit assessment and some adaptive measures. Underlying immunosuppression, elevated cytokine levels, altered expression of the angiotensin converting enzyme (ACE-2) and TMPRSS2, and a prothrombotic status may fuel the effects of a SARS-CoV-2 in some cancer patients, but have the potential to be used as biomarkers for severe disease and therapeutic targets. The rapidly expanding literature on COVID-19 should be interpreted with care as it is often hampered by methodological and statistical flaws.

The Sialoside-Binding Pocket of SARS-CoV-2 Spike Glycoprotein Structurally Resembles MERS-CoV

COVID-19 novel coronavirus (CoV) disease caused by severe acquired respiratory syndrome (SARS)-CoV-2 manifests severe lethal respiratory illness in humans and has recently developed into a worldwide pandemic. The lack of effective treatment strategy and vaccines against the SARS-CoV-2 poses a threat to human health. An extremely high infection rate and multi-organ secondary infection within a short period of time makes this virus more deadly and challenging for therapeutic interventions. Despite high sequence similarity and utilization of common host-cell receptor, human angiotensin-converting enzyme-2 (ACE2) for virus entry, SARS-CoV-2 is much more infectious than SARS-CoV. Structure-based sequence comparison of the N-terminal domain (NTD) of the spike protein of Middle East respiratory syndrome (MERS)-CoV, SARS-CoV, and SARS-CoV-2 illustrate three divergent loop regions in SARS-CoV-2, which is reminiscent of MERS-CoV sialoside binding pockets. Comparative binding analysis with host sialosides revealed conformational flexibility of SARS-CoV-2 divergent loop regions to accommodate diverse glycan-rich sialosides. These key differences with SARS-CoV and similarity with MERS-CoV suggest an evolutionary adaptation of SARS-CoV-2 spike glycoprotein reciprocal interaction with host surface sialosides to infect host cells with wide tissue tropism.

The neurology of COVID-19 revisited: A proposal from the Environmental Neurology Specialty Group of the World Federation of Neurology to implement international neurological registries

A comprehensive review of the neurological disorders reported during the current COVID-19 pandemic demonstrates that infection with SARS-CoV-2 affects the central nervous system (CNS), the peripheral nervous system (PNS) and the muscle. CNS manifestations include: headache and decreased responsiveness considered initial indicators of potential neurological involvement; anosmia, hyposmia, hypogeusia, and dysgeusia are frequent early symptoms of coronavirus infection. Respiratory failure, the lethal manifestation of COVID-19, responsible for 264,679 deaths worldwide, is probably neurogenic in origin and may result from the viral invasion of cranial nerve I, progressing into rhinencephalon and brainstem respiratory centers. Cerebrovascular disease, in particular large-vessel ischemic strokes, and less frequently cerebral venous thrombosis, intracerebral hemorrhage and subarachnoid hemorrhage, usually occur as part of a thrombotic state induced by viral attachment to ACE2 receptors in endothelium causing widespread endotheliitis, coagulopathy, arterial and venous thromboses. Acute hemorrhagic necrotizing encephalopathy is associated to the cytokine storm. A frontal hypoperfusion syndrome has been identified. There are isolated reports of seizures, encephalopathy, meningitis, encephalitis, and myelitis. The neurological diseases affecting the PNS and muscle in COVID-19 are less frequent and include Guillain-Barré syndrome; Miller Fisher syndrome; polyneuritis cranialis; and rare instances of viral myopathy with rhabdomyolysis. The main conclusion of this review is the pressing need to define the neurology of COVID-19, its frequency, manifestations, neuropathology and pathogenesis. On behalf of the World Federation of Neurology we invite national and regional neurological associations to create local databases to report cases with neurological manifestations observed during the on-going pandemic. International neuroepidemiological collaboration may help define the natural history of this worldwide problem.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review (Preprint).. [DOI: 10.2196/preprints.20756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.

OBJECTIVE

This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.

METHODS

A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.

RESULTS

We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.

CONCLUSIONS

The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov's complexity and Shannon's information theories

This paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen-Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.