Covid-19 Grouping Insights - Principal Component Analysis

Pattern and Group behavior of COVID-19 patient's data is very much important aspect of data analysis for medical stakeholders to frame decision strategies and to design routine set-ups. PCA is old mathematical machine learning grouping analysis technique but popular in recently for data analysis in grouping insights, so the researcher has implemented principal component analysis to study dominance in data with varied component grouping with proportional variation and also used graphical visualizations. The strength of PCA implementation is to maintain real valued data with dimension reduction but without loss of key information. The experiment result of COVID-19 datais showing component 1 contributing to highly positive testResult that is having High impact on patients. PCA shows relative dimensions analysis. PCA dimensions plotted shows independence in dimension with 900 angle dimensions in data spread. The results identified for COVID-19 data glimpse with dominant component formation. Copyright © 2022 Ubiquity Press. All rights reserved.

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil.

The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors.

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

Nature's therapy for COVID-19: Targeting the vital non-structural proteins (NSP) from SARS-CoV-2 with phytochemicals from Indian medicinal plants.

Background: Containing COVID-19 is still a global challenge. It has affected the "normal" world by targeting its economy and health sector. The effect is shifting of focus of research from life threatening diseases like cancer. Thus, we need to develop a medical solution at the earliest. The purpose of this present work was to understand the efficacy of 22 rationally screened phytochemicals from Indian medicinal plants obtained from our previous work, following drug-likeness properties, against 6 non-structural-proteins (NSP) from SARS-CoV-2. Methods: 100 ns molecular dynamics simulations were performed, and relative binding free energies were computed by MM/PBSA. Further, principal component analysis, dynamic cross correlation and hydrogen bond occupancy were analyzed to characterize protein-ligand interactions. Biological pathway enrichment analysis was also carried out to elucidate the therapeutic targets of the phytochemicals in comparison to SARS-CoV-2. Results: The potential binding modes and favourable molecular interaction profile of 9 phytochemicals, majorly from Withania somnifera with lowest free binding energies, against the SARS-CoV-2 NSP targets were identified. It was understood that phytochemicals and 2 repurposed drugs with steroidal moieties in their chemical structures formed stable interactions with the NSPs. Additionally, human target pathway analysis for SARS-CoV-2 and phytochemicals showed that cytokine mediated pathway and phosphorylation pathways were with the most significant p-value. Conclusions: To summarize this work, we suggest a global approach of targeting multiple proteins of SARS-CoV-2 with phytochemicals as a natural alternative therapy for COVID-19. We also suggest that these phytochemicals need to be tested experimentally to confirm their efficacy.

A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019.

BACKGROUND AND AIMS: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. METHODS: Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. RESULTS: We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. CONCLUSION: This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

A comparative genomics-based study of positive strand RNA viruses emphasizing on SARS-CoV-2 utilizing dinucleotide signature, codon usage and codon context analyses.

The novel corona virus disease or COVID-19 caused by a positive strand RNA virus (PRV) called SARS-CoV-2 is plaguing the entire planet as we conduct this study. In this study a multifaceted analysis was carried out employing dinucleotide signature, codon usage and codon context to compare and unravel the genomic as well as genic characteristics of the SARS-CoV-2 isolates and how they compare to other PRVs which represents some of the most pathogenic human viruses. The main emphasis of this study was to comprehend the codon biology of the SARS-CoV-2 in the backdrop of the other PRVs like Poliovirus, Japanese encephalitis virus, Hepatitis C virus, Norovirus, Rubella virus, Semliki Forest virus, Zika virus, Dengue virus, Human rhinoviruses and the Betacoronaviruses since codon usage pattern along with the nucleotide composition prevalent within the viral genome helps to understand the biology and evolution of viruses. Our results suggest discrete genomic dinucleotide signature within the PRVs. Some of the genes from the different SARS-CoV-2 isolates were also found to demonstrate heterogeneity in terms of their dinucleotide signature. The SARS-CoV-2 isolates also demonstrated a codon context trend characteristically dissimilar to the other PRVs. The findings of this study are expected to contribute to the developing global knowledge base in countering COVID-19.