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影响范围:预测免疫反应和对流感病毒的易感性——愿数据与你同在。

mSphere of Influence: Predicting Immune Responses and Susceptibility to Influenza Virus-May the Data Be with You.

机构信息

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

出版信息

mSphere. 2020 Mar 18;5(2):e00085-20. doi: 10.1128/mSphere.00085-20.

DOI:10.1128/mSphere.00085-20
PMID:32188748
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7082138/
Abstract

Irene Ramos works in the field of immunology to viral infections. In this mSphere of Influence article, she reflects on how "Global analyses of human immune variation reveal baseline predictors of postvaccination responses" by Tsang et al. (Cell 157:499-513, 2014, https://doi.org/10.1016/j.cell.2014.03.031) and "A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection" by Fourati et al. (Nat Commun 9:4418, 2018, https://doi.org/10.1038/s41467-018-06735-8) made an impact on her by highlighting the importance of data science methods to understand virus-host interactions.

摘要

Irene Ramos 从事免疫学领域的病毒感染研究。在这篇 mSphere 影响力文章中,她反思了 Tsang 等人的研究成果“全球人类免疫变异分析揭示了疫苗接种后反应的基线预测因素”(Cell 157:499-513, 2014, https://doi.org/10.1016/j.cell.2014.03.031)和 Fourati 等人的研究“一项众包分析,旨在确定预测易感性病毒感染的从头分子特征”(Nat Commun 9:4418, 2018, https://doi.org/10.1038/s41467-018-06735-8)对她产生了影响,强调了数据科学方法对于理解病毒-宿主相互作用的重要性。

相似文献

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mSphere of Influence: Predicting Immune Responses and Susceptibility to Influenza Virus-May the Data Be with You.影响范围:预测免疫反应和对流感病毒的易感性——愿数据与你同在。
mSphere. 2020 Mar 18;5(2):e00085-20. doi: 10.1128/mSphere.00085-20.
2
mSphere of Influence: Redefining an Influenza Virus-How Different Are Influenza Viruses and Extracellular Vesicles?影响范围:重新定义流感病毒——流感病毒与细胞外囊泡有何不同?
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mSphere of Influence: Understanding Virus-Host Interactions Requires a Multifaceted Approach.影响范围:理解病毒-宿主相互作用需要多方面的方法。
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mSphere of Influence: Considering Complex Mutational Processes That Shape Microbial Virulence.影响范围:考虑塑造微生物毒力的复杂突变过程。
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引用本文的文献

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Predictive signatures of immune response to vaccination and implications of the immune setpoint remodeling.疫苗接种免疫反应的预测特征及免疫设定点重塑的影响。
mSphere. 2025 Feb 25;10(2):e0050224. doi: 10.1128/msphere.00502-24. Epub 2025 Jan 24.
2
The Whole Body as the System in Systems Immunology.全身作为系统免疫学中的系统
iScience. 2020 Aug 28;23(9):101509. doi: 10.1016/j.isci.2020.101509. eCollection 2020 Sep 25.

本文引用的文献

1
Multi-Level Model to Predict Antibody Response to Influenza Vaccine Using Gene Expression Interaction Network Feature Selection.使用基因表达相互作用网络特征选择预测流感疫苗抗体反应的多层次模型
Microorganisms. 2019 Mar 14;7(3):79. doi: 10.3390/microorganisms7030079.
2
A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.一项众包分析,旨在确定可预测易感性病毒感染的从头分子特征。
Nat Commun. 2018 Oct 24;9(1):4418. doi: 10.1038/s41467-018-06735-8.
3
Multiple network-constrained regressions expand insights into influenza vaccination responses.多种网络约束回归扩展了对流感疫苗接种反应的认识。
Bioinformatics. 2017 Jul 15;33(14):i208-i216. doi: 10.1093/bioinformatics/btx260.
4
Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses.多队列分析揭示了流感疫苗接种反应的基线转录预测指标。
Sci Immunol. 2017 Aug 25;2(14). doi: 10.1126/sciimmunol.aal4656.
5
Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination.基因表达的衰老依赖性改变以及对人流感疫苗反应的线粒体特征。
Aging (Albany NY). 2015 Jan;7(1):38-52. doi: 10.18632/aging.100720.
6
Heme on innate immunity and inflammation.血红素与固有免疫和炎症
Front Pharmacol. 2014 May 27;5:115. doi: 10.3389/fphar.2014.00115. eCollection 2014.
7
Global analyses of human immune variation reveal baseline predictors of postvaccination responses.全球人类免疫变异分析揭示了疫苗接种后反应的基线预测因子。
Cell. 2014 Apr 10;157(2):499-513. doi: 10.1016/j.cell.2014.03.031.