Identifying Drug-Induced Liver Injury Associated With Inflammation-Drug and Drug-Drug Interactions in Pharmacologic Treatments for COVID-19 by Bioinformatics and System Biology Analyses: The Role of Pregnane X Receptor

Huang, Jingjing; Zhang, Zhaokang; Hao, Chenxia; Qiu, Yiwen; Tan, Ruoming; Liu, Jialin; Wang, Xiaoli; Yang, Wei; Qu, Hongping

doi:10.3389/fphar.2022.804189

Cited by 3 publications

(2 citation statements)

References 88 publications

(105 reference statements)

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“…Lastly, drug-elicited liver injury may also account for laboratory test abnormalities. 3,24 However, there was no significant difference in drug use among the liver injury group, abnormal group and normal group in this study, except for a vasoactive drug. However, the vasoactive drug was not an independent risk factor for the occurrence of liver injury in the logistic analysis.…”

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confidence: 56%

Clinical Course and Risk Factors for Liver Injury of Severe and Critical Patients with COVID-19

Du¹,

Yang²,

Li³

et al. 2022

IDR

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Introduction Information regarding the clinical course of COVID-19 patients with liver injury is very limited, especially in severe and critical patients. The objective of this study was to describe the characteristics and clinical course of liver function in patients admitted with severe and/or critical SARS-CoV-2 infection, as well as explore the risk factors that affect liver function in the enrolled COVID-19 patients. Methods Information on clinical characteristics of 63 severe and critical patients with confirmed COVID-19 was collected. Data on patients’ demographics, laboratory characteristics, laboratory examination, SARS-CoV-2 RNA results and liver test parameters were acquired and analyzed. Results The incidence of abnormal aspartate aminotransferase, alanine aminotransferase, and total bilirubin in the critical group was significantly higher than in the severe group (respectively 81.48%, 81.49%, 62.67%, and 45.71%, 63.88%, 22.86%, p < 0.05). The time for liver function parameters to reach their extremes was approximately 2–3 weeks after admission. The independent factors associated with liver injury were patients with invasive ventilators, decreased percentages of neutrophils, lymphocytes and monocytes, and sequential organ failure assessment (SOFA) score ≥2 (p < 0.05). Conclusion Abnormal liver tests are commonly observed in severe and critical patients with COVID-19. Severe patients infected by SARS-CoV-2 should be closely observed and monitored the liver function parameters, particularly when they present with independent risk factors for liver injury.

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confidence: 56%

Clinical Course and Risk Factors for Liver Injury of Severe and Critical Patients with COVID-19

Du¹,

Yang²,

Li³

et al. 2022

IDR

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“…Importantly, NRF2 activation has been shown to benefit respiratory infections in various animal models (Muchtaridi et al, 2022). NRF2 exerts anti-inflammatory effects by inhibiting pro-inflammatory genes such as IL6 and IL1B (Huang et al, 2022). NRF2 induces the expression of genes that promote specificity of macrophages such as the macrophage receptor, which is responsible for bacterial phagocytosis (Schaefer et al, 2022), and the cluster of differentiation gene 36 (CD36), which resists viral infection (Hillier et al, 2022).…”

Section: Top Features Identified Via Multiple Methodsmentioning

confidence: 99%

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods

Ren

Liao³

et al. 2023

Front. Microbiol.

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To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body’s immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2–4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1–4, and days 7–10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.

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In Silico Protein Structure Analysis for SARS-CoV-2 Vaccines Using Deep Learning

Matsuzaka

Yashiro

2023

BioMedInformatics

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Protein three-dimensional structural analysis using artificial intelligence is attracting attention in various fields, such as the estimation of vaccine structure and stability. In particular, when using the spike protein in vaccines, the major issues in the construction of SARS-CoV-2 vaccines are their weak abilities to attack the virus and elicit immunity for a short period. Structural information about new viruses is essential for understanding their properties and creating effective vaccines. However, determining the structure of a protein through experiments is a lengthy and laborious process. Therefore, a new computational approach accelerated the elucidation process and made predictions more accurate. Using advanced machine learning technology called deep neural networks, it has become possible to predict protein structures directly from protein and gene sequences. We summarize the advances in antiviral therapy with the SARS-CoV-2 vaccine and extracellular vesicles via computational analysis.

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Identifying Drug-Induced Liver Injury Associated With Inflammation-Drug and Drug-Drug Interactions in Pharmacologic Treatments for COVID-19 by Bioinformatics and System Biology Analyses: The Role of Pregnane X Receptor

Cited by 3 publications

References 88 publications

Clinical Course and Risk Factors for Liver Injury of Severe and Critical Patients with COVID-19

Clinical Course and Risk Factors for Liver Injury of Severe and Critical Patients with COVID-19

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods

In Silico Protein Structure Analysis for SARS-CoV-2 Vaccines Using Deep Learning

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