A 20-Gene Set Predictive of Progression to Severe Dengue (Q61806871)

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21 April 2020

title

A 20-Gene Set Predictive of Progression to Severe Dengue (English)

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21 April 2020

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Timothy E Sweeney

2

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21 April 2020

Rina Barouch-Bentov

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Malaya Kumar Sahoo

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Larry Kalesinskas

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Francesco Vallania

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Ana Maria Sanz

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Eliana Ortiz-Lasso

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Ludwig Luis Albornoz

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Fernando Rosso

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Jose G Montoya

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Benjamin A Pinsky

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Shirit Einav

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language of work or name

English

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publication date

1 January 2019

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volume

26

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issue

5

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21 April 2020

page(s)

1104-1111.e4

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Receptor-type protein-tyrosine phosphatase mu is expressed in specific vascular endothelial beds in vivo

21 April 2020

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Temporal dynamics of the transcriptional response to dengue virus infection in Nicaraguan children

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Sequential waves of gene expression in patients with clinically defined dengue illnesses reveal subtle disease phases and predict disease severity

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Dengue virus infection induces expansion of a CD14(+)CD16(+) monocyte population that stimulates plasmablast differentiation

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Time since onset of disease and individual clinical markers associate with transcriptional changes in uncomplicated dengue

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Multiplexed locus-specific analysis of DNA methylation in single cells.

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A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set

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Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses

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Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis

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Single-Reaction Multiplex Reverse Transcription PCR for Detection of Zika, Chikungunya, and Dengue Viruses

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Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity

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Chymase is a Predictive Biomarker of Dengue Hemorrhagic Fever in Pediatric and Adult Patients

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KLRD1-expressing natural killer cells predict influenza susceptibility.

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Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip

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A multi-cohort study of the immune factors associated with M. tuberculosis infection outcomes

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12 December 2020

Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

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12 December 2020

10.1016/J.CELREP.2019.01.033

Identifiers

DOI

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https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:30699342%20AND%20SRC:MED&resulttype=core&format=json

21 April 2020

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https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:30699342%20AND%20SRC:MED&resulttype=core&format=json

21 April 2020

PubMed publication ID

30699342

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