Evidence map›Paper›PMID 33361110›Full record

ArticleLife science alliance2021

Profiling of immune dysfunction in COVID-19 patients allows early prediction of disease progression.

André F Rendeiro, Joseph Casano, Charles Kyriakos Vorkas, Harjot Singh, Ayana Morales, Robert A DeSimone, Grant B Ellsworth, Rosemary Soave, Shashi N Kapadia, Kohta Saito and 12 more

Open access · goldAbstract read
In one paragraph

Article in Life science alliance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.

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  20. Suppressive myeloid cells in SARS-CoV-2 andFrontiers in immunology · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors at 2 institutions in 2 countries.

André F RendeiroInstitute of Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
Joseph CasanoDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Charles Kyriakos VorkasDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Harjot SinghDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Ayana MoralesDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Robert A DeSimoneDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0002-3484-790X
Grant B EllsworthDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0002-7096-5153
Rosemary SoaveDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Shashi N KapadiaDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Kohta SaitoDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
Christopher D BrownDivision of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0003-2182-8133
JingMei HsuDivision of Hematology/Oncology, Department of Medicine Weill Cornell Medicine, New York, NY, USA.
Christopher KyriakidesDepartment of Rehabilitation Medicine at New York University Grossman School of Medicine New York, NY, USA.
Steven ChiuDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Luca Vincenzo CappelliDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.ORCID 0000-0001-8090-3880
Maria Teresa CacciapuotiDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Wayne TamDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Lorenzo GalluzziCaryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
Paul D SimonsonDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
Olivier ElementoInstitute of Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA ole2001@med.cornell.edu.
Mirella SalvatoreDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA mis2053@med.cornell.edu.ORCID 0000-0002-8296-0376
Giorgio InghiramiDepartment of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA ggi9001@med.cornell.edu.ORCID 0000-0001-5566-0864
Cornell University · USNew York University · US

Funding

Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Institutional Career Development CoreKL2TR002385 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI YUAN-SHAN ZHU · 2017 to 2026
$13.3M
T32: Pathogenesis of Infectious Diseases Training ProgramT32AI007613 · NIAID · WEILL MEDICAL COLL OF CORNELL UNIV · PI ROY M. GULICK · 1999 to 2026
$8.8M
Molecular and Translational Oncology ResearchT32CA203702 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI BLENIS, JOHN, GIANNAKAKOU, PARASKEVI · 2017 to 2021
$1.4M
The role of Mucosal-associated invariant T (MAIT) cells during the innate immune response to Mycobacterium tuberculosisK08AI132739 · NIAID · WEILL MEDICAL COLL OF CORNELL UNIV · PI VORKAS, CHARLES KYRIAKOS · 2018 to 2022
$921k
Identifying the Molecular Target of Two Novel Compounds Active Against Non-replicating Mycobacterium tuberculosis and Delineating the Role of the Putative Target, InhA, in Non-replicating StatesK08AI139360 · NIAID · WEILL MEDICAL COLL OF CORNELL UNIV · PI SAITO, KOHTA · 2019 to 2023
$860k
NCATS NIH HHS KL2 TR002385NCATS NIH HHS UL1 TR002384NCI NIH HHS T32 CA203702NIAID NIH HHS K08 AI132739NIAID NIH HHS K08 AI139360NIAID NIH HHS T32 AI007613NIAID NIH HHS T32 AI07613-19
6 · The paper itself

Abstract

With a rising incidence of COVID-19-associated morbidity and mortality worldwide, it is critical to elucidate the innate and adaptive immune responses that drive disease severity. We performed longitudinal immune profiling of peripheral blood mononuclear cells from 45 patients and healthy donors. We observed a dynamic immune landscape of innate and adaptive immune cells in disease progression and absolute changes of lymphocyte and myeloid cells in severe versus mild cases or healthy controls. Intubation and death were coupled with selected natural killer cell KIR receptor usage and IgM+ B cells and associated with profound CD4 and CD8 T-cell exhaustion. Pseudo-temporal reconstruction of the hierarchy of disease progression revealed dynamic time changes in the global population recapitulating individual patients and the development of an eight-marker classifier of disease severity. Estimating the effect of clinical progression on the immune response and early assessment of disease progression risks may allow implementation of tailored therapies.

Indexed as

Adaptive ImmunityAdultAgedAged, 80 and overCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCOVID-19Disease ProgressionEpidemicsFemaleHumansImmune System DiseasesImmunity, InnateLymphocyte SubsetsMaleMiddle Aged

Identifiers

PMID33361110
PMCPMC7768198
OpenAlexW3115578864

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.