Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases

Satu, Shahriare; Khan, Md. Imran; Rahman, Rezanur; Howlader, Koushik Chandra; Roy, Subrata Chandra; Roy, Shuvo Saha; Quinn, Julian M.W.; Moni, Mohammad Ali

doi:10.1093/bib/bbab003

Cited by 39 publications

(22 citation statements)

References 46 publications

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“…Intensive care units (ICUs) are key to increasing the survival of patients with severe COVID-19; they provide oxygen, 24-hour monitoring and care, and assisted ventilation when needed. Therefore, ICU beds are a precious resource in locations where COVID-19 case numbers are high [6][7][8]. Allocating hospital wards or ICU beds for infected patients thus requires rapid decision-making processes, both to use resources efficiently and reduce patient suffering and mortality.…”

Section: Introductionmentioning

confidence: 99%

“…Rapid blood and serology testing of clinical samples by current equipment enables monitoring of many peripheral blood parameters of interest, some of which indicate changes in organ functions and are used to diagnose a range of conditions and diseases [7,12]. This raises the possibility that such profiling of blood samples could provide predictive information about the disease trajectory and risk of comorbidities for patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Aktar¹,

Ahamad²,

Rashed-Al-Mahfuz³

et al. 2021

JMIR Med Inform

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Background Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. Conclusions We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Aktar¹,

Ahamad²,

Rashed-Al-Mahfuz³

et al. 2021

JMIR Med Inform

Self Cite

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show abstract

“…Our methodology is implemented in an R programming platform that incorporates several other packages from the Bioconductor repository, although these can be easily substituted with another implementation using a different platform. From the methodological point of view, such approaches have been successfully demonstrated various disease interactions recently [ 41 , 87 ]. It’s noteworthy, however, that the dataset selection would have some qualitative and quantitative effects on the outcomes.…”

Section: Discussionmentioning

confidence: 99%

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

et al. 2021

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Alzheimer’s disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

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“…Another respiratory infection, respiratory syncytial virus (RSV), enhances the expression of Prdx2 [ 153 ]. Data on the effect of coronaviruses on peroxiredoxins remain vague, although expression of Prdx3 during SARS-CoV-2 expression could be affected [ 154 ].…”

Section: Peroxiredoxins and Viral Infectionsmentioning

confidence: 99%

Peroxiredoxins—The Underrated Actors during Virus-Induced Oxidative Stress

et al. 2021

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Enhanced production of reactive oxygen species (ROS) triggered by various stimuli, including viral infections, has attributed much attention in the past years. It has been shown that different viruses that cause acute or chronic diseases induce oxidative stress in infected cells and dysregulate antioxidant its antioxidant capacity. However, most studies focused on catalase and superoxide dismutases, whereas a family of peroxiredoxins (Prdx), the most effective peroxide scavengers, were given little or no attention. In the current review, we demonstrate that peroxiredoxins scavenge hydrogen and organic peroxides at their physiological concentrations at various cell compartments, unlike many other antioxidant enzymes, and discuss their recycling. We also provide data on the regulation of their expression by various transcription factors, as they can be compared with the imprint of viruses on transcriptional machinery. Next, we discuss the involvement of peroxiredoxins in transferring signals from ROS on specific proteins by promoting the oxidation of target cysteine groups, as well as briefly demonstrate evidence of nonenzymatic, chaperone, functions of Prdx. Finally, we give an account of the current state of research of peroxiredoxins for various viruses. These data clearly show that Prdx have not been given proper attention despite all the achievements in general redox biology.

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Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases

Cited by 39 publications

References 46 publications

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Peroxiredoxins—The Underrated Actors during Virus-Induced Oxidative Stress

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