The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

Safety and efficacy of sirolimus in hospitalised patients with COVID-19 pneumonia

BACKGROUND

There is a critical need to develop novel therapies for COVID-19.

METHODS

We conducted a phase 2, multicentre, placebo-controlled, double-blind, randomised trial; hospitalised patients with hypoxemic respiratory failure due to COVID-19 and at least one poor prognostic biomarker, were given sirolimus (6 mg on Day 1 followed by 2 mg daily for 14 days or hospital discharge, whichever happens first) or placebo, in a 2:1 randomization scheme favouring sirolimus. Primary outcome was the proportion of patients alive and free from advanced respiratory support measures at Day 28.

RESULTS

Between April 2020 and April 2021, 32 patients underwent randomization and 28 received either sirolimus (n = 18) or placebo (n = 10). Mean age was 57 years and 75 % of the subjects were men. Twenty-two subjects had at least one co-existing condition (Diabetes, hypertension, obesity, CHF, or asthma/COPD) associated with worse prognosis. Mean FiO2 requirement was 0.35. There was no difference in the proportion of patients who were alive and free from advanced respiratory support measures in the sirolimus group (n = 15, 83 %) compared with the placebo group (n = 8, 80 %). Although patients in the sirolimus group demonstrated faster improvement in oxygenation and spent less time in the hospital, these differences were not statistically significant. There was no between-group difference in the rate of change in serum biomarkers such as LDH, ferritin, d-dimer or lymphocyte count. There was a decreased risk of thromboembolic complications in patients on sirolimus compared with placebo.

CONCLUSIONS

Larger studies are warranted to evaluate the role sirolimus in COVID-19 infection.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

SARS-CoV-2 modulates inflammatory responses of alveolar epithelial type II cells via PI3K/AKT pathway

BACKGROUND

SARS-CoV-2 infects through the respiratory route and triggers inflammatory response by affecting multiple cell types including type II alveolar epithelial cells. SARS-CoV-2 triggers signals via its Spike (S) protein, which have been shown to participate in the pathogenesis of COVID19.

AIM

Aim of the present study was to investigate the effect of SARS-CoV2 on type II alveolar epithelial cells, focusing on signals initiated by its S protein and their impact on the expression of inflammatory mediators.

RESULTS

For this purpose A549 alveolar type II epithelial cells were exposed to SARS CoV2 S recombinant protein and the expression of inflammatory mediators was measured. The results showed that SARS-CoV-2 S protein decreased the expression and secretion of IL8, IL6 and TNFα, 6 hours following stimulation, while it had no effect on IFNα, CXCL5 and PAI-1 expression. We further examined whether SARS-CoV-2 S protein, when combined with TLR2 signals, which are also triggered by SARS-CoV2 and its envelope protein, exerts a different effect in type II alveolar epithelial cells. Simultaneous treatment of A549 cells with SARS-CoV-2 S protein and the TLR2 ligand PAM3csk4 decreased secretion of IL8, IL6 and TNFα, while it significantly increased IFNα, CXCL5 and PAI-1 mRNA expression. To investigate the molecular pathway through which SARS-CoV-2 S protein exerted this immunomodulatory action in alveolar epithelial cells, we measured the induction of MAPK/ERK and PI3K/AKT pathways and found that SARS-CoV-2 S protein induced the activation of the serine threonine kinase AKT. Treatment with the Akt inhibitor MK-2206, abolished the inhibitory effect of SARS-CoV-2 S protein on IL8, IL6 and TNFα expression, suggesting that SARS-CoV-2 S protein mediated its action via AKT kinases.

CONCLUSION

The findings of our study, showed that SARS-CoV-2 S protein suppressed inflammatory responses in alveolar epithelial type II cells at early stages of infection through activation of the PI3K/AKT pathway. Thus, our results suggest that at early stages SARS-CoV-2 S protein signals inhibit immune responses to the virus allowing it to propagate the infection while in combination with TLR2 signals enhances PAI-1 expression, potentially affecting the local coagulation cascade.

KG-Predict: A knowledge graph computational framework for drug repurposing

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).

Biomedical Relation Extraction with Knowledge Graph-based Recommendations

Biomedical Relation Extraction (RE) systems identify and classify relations between biomedical entities to enhance our knowledge of biological and medical processes. Most state-of-the-art systems use deep learning approaches, mainly to target relations between entities of the same type, such as proteins or pharmacological substances. However, these systems are mostly restricted to what they directly identify on the text and ignore specialized domain knowledge bases, such as ontologies, that formalize and integrate biomedical information typically structured as direct acyclic graphs. On the other hand, Knowledge Graph (KG)-based recommendation systems already showed the importance of integrating KGs to add additional features to items. Typical systems have users as people and items that can range from movies to books, which people saw or read and classified according to their satisfaction rate. This work proposes to integrate KGs into biomedical RE through a recommendation model to further improve their range of action. We developed a new RE system, named K-BiOnt, by integrating a baseline state-of-the-art deep biomedical RE system with an existing KG-based recommendation state-of-the-art system. Our results show that adding recommendations from KG-based recommendation improves the system's ability to identify true relations that the baseline deep RE model could not extract from the text. All the software and data supporting our work will be made publicly available upon acceptance of this manuscript.

Diabetes and COVID-19: The potential role of mTOR

Diabetes is the most frequent comorbidity among patients with COVID-19. COVID-19 patients with diabetes have a more severe prognosis than patients without diabetes. However, the etiopathogenetic mechanisms underlying this more unfavorable outcome in these patients are not clear. Probably the etiopathogenetic mechanisms underlying diabetes could represent a favorable substrate for a greater development of the inflammatory process already dysregulated in COVID-19 with a more severe evolution of the disease. In the attempt to shed light on the possible etiopathogenetic mechanisms, we wanted to evaluate the possible role of mTOR (mammalian Target Of Rapamycin) pathway in this context. We searched the PubMed and Scopus databases to identify articles involving diabetes and the mTOR pathway in COVID-19. The mTOR pathway could be involved in this etiopathogenetic mechanism, in particular, the activation and stimulation of this pathway could favor an inflammatory process that is already dysregulated in itself, while its inhibition could be a way to regulate this dysregulated inflammatory process. However, much remains to be clarified about the mechanisms of the mTOR pathway and its role in COVID-19. The aim of this review is to to understand the etiopathogenesis underlying COVID-19 in diabetic patients and the role of mTOR pathway in order to be able to search for new weapons to deal with this disease.

Clinical informatics solutions in COVID-19 pandemic: Scoping literature review

Background

The global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak.

Methods

A comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis.

Results

In this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%).

Conclusion

The present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.

COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments

Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Objectives: To analyze the content of publications within the medical NLP domain in 2020.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included.

Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)-the branch of artificial intelligence that interprets human language-can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Objective

To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

Materials and Methods

We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.

Results

In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.

Discussion

Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.

Conclusion

There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

Rapamycin: Drug Repurposing in SARS-CoV-2 Infection

Since December 2019, SARS-CoV-2 (COVID-19) has been a worldwide pandemic with enormous consequences for human health and the world economy. Remdesivir is the only drug in the world that has been approved for the treating of COVID-19. This drug, as well as vaccination, still has uncertain effectiveness. Drug repurposing could be a promising strategy how to find an appropriate molecule: rapamycin could be one of them. The authors performed a systematic literature review of available studies on the research describing rapamycin in association with COVID-19 infection. Only peer-reviewed English-written articles from the world’s acknowledged databases Web of Science, PubMed, Springer and Scopus were involved. Five articles were eventually included in the final analysis. The findings indicate that rapamycin seems to be a suitable candidate for drug repurposing. In addition, it may represent a better candidate for COVID-19 therapy than commonly tested antivirals. It is also likely that its efficiency will not be reduced by the high rate of viral RNA mutation.

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at¹ and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.

Analysis and Evaluation of COVID-19 Web Applications for Health Professionals: Challenges and Opportunities

The multidisciplinary nature of the work required for research in the COVID-19 pandemic has created new challenges for health professionals in the battle against the virus. They need to be equipped with novel tools, applications, and resources-that have emerged during the pandemic-to gain access to breakthrough findings; know the latest developments; and to address their specific needs for rapid data acquisition, analysis, evaluation, and reporting. Because of the complex nature of the virus, healthcare systems worldwide are severely impacted as the treatment and the vaccine for COVID-19 disease are not yet discovered. This leads to frequent changes in regulations and policies by governments and international organizations. Our analysis suggests that given the abundance of information sources, finding the most suitable application for analysis, evaluation, or reporting, is one of such challenges. However, health professionals and policy-makers need access to the most relevant, reliable, trusted, and latest information and applications that can be used in their day-to-day tasks of COVID-19 research and analysis. In this article, we present our analysis of various novel and important web-based applications that have been specifically developed during the COVID-19 pandemic and that can be used by the health professionals community to help in advancing their analysis and research. These applications comprise search portals and their associated information repositories for literature and clinical trials, data sources, tracking dashboards, and forecasting models. We present a list of the minimally essential online, web-based applications to serve a multitude of purposes, from hundreds of those developed since the beginning of the pandemic. A critical analysis is provided for the selected applications based on 17 features that can be useful for researchers and analysts for their evaluations. These features make up our evaluation framework and have not been used previously for analysis and evaluation. Therefore, knowledge of these applications will not only increase productivity but will also allow us to explore new dimensions for using existing applications with more control, better management, and greater outcome of their research. In addition, the features used in our framework can be applied for future evaluations of similar applications and health professionals can adapt them for evaluation of other applications not covered in this analysis.