Evidence map›Paper›PMID 35105981›Full record

ReviewNature immunology2022

Innate immunity: the first line of defense against SARS-CoV-2.

Michael S Diamond, Thirumala-Devi Kanneganti

Abstract readReview
In one paragraph

Review in Nature immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 358 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
358citing papers in PubMed, 5 pooled it
–field-weighted citation impact
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

358 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Impact of air pollution on COVID-19 severity: a systematic review of underlying biological mechanisms.European respiratory review : an official journal of the European Respiratory Society · 2025
    Pooled it
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298 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michael S DiamondDepartment of Medicine, Washington University School of Medicine, St. Louis, St. Louis, MO, USA.ORCID http://orcid.org/0000-0002-8791-3165
Thirumala-Devi KannegantiDepartment of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA. thirumala-devi.kanneganti@stjude.org.ORCID http://orcid.org/0000-0002-6395-6443

Funding

NIAID Centers of Excellence for Influenza Research and Response: Universal Influenza Vaccine Research Activities75N93021C00014 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GARCIA-SASTRE, ADOLFO · 2021 to 2025
$62.6M
Innate immune sensors, inflammasomes, and inflammasome-mediated processes in cancerR35CA253095 · NCI · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI Thirumala-Devi Kanneganti · 2020 to 2026
$7.0M
Human antibody-based countermeasures against the Wuhan Coronavirus SARS-CoV-2R01AI157155 · NIAID · WASHINGTON UNIVERSITY · PI BARIC, RALPH S, CROWE, JAMES E · 2020 to 2024
$6.0M
Role of NLR Inflammasomes in Autoinflammatory Diseases and Host DefenseR01AR056296 · NIAMS · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI KANNEGANTI, THIRUMALA-DEVI · 2008 to 2022
$5.9M
The Non-Inflammasome NLRs in Immunity and Host defenseR01AI124346 · NIAID · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI Thirumala-Devi Kanneganti · 2016 to 2026
$5.8M
Inflammatory Caspases in Innate Immunity and InflammationR37AI101935 · NIAID · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI Thirumala-Devi Kanneganti · 2017 to 2026
$4.9M
Targeting innate immune pathways, and inflammatory cell death in cytokine-mediated diseasesR01AI160179 · NIAID · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI KANNEGANTI, THIRUMALA-DEVI · 2021 to 2025
$4.2M
Inflammatory Caspases in Innate Immunity and InflammationR01AI101935 · NIAID · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI KANNEGANTI, THIRUMALA-DEVI · 2012 to 2016
$2.2M
American Lebanese Syrian Associated Charities (ALSAC) N/ANCI NIH HHS R35 CA253095NIAID NIH HHS 75N93021C00014NIAID NIH HHS R01 AI101935NIAID NIH HHS R01 AI124346NIAID NIH HHS R01 AI157155NIAID NIH HHS R01 AI160179NIAID NIH HHS R37 AI101935NIAMS NIH HHS R01 AR056296U.S. Department of Health & Human Services | National Institutes of Health (NIH) AI101935U.S. Department of Health & Human Services | National Institutes of Health (NIH) AI124346U.S. Department of Health & Human Services | National Institutes of Health (NIH) AI157155U.S. Department of Health & Human Services | National Institutes of Health (NIH) AI160179U.S. Department of Health & Human Services | National Institutes of Health (NIH) AR056296U.S. Department of Health & Human Services | National Institutes of Health (NIH) CA253095
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2, continues to cause substantial morbidity and mortality. While most infections are mild, some patients experience severe and potentially fatal systemic inflammation, tissue damage, cytokine storm and acute respiratory distress syndrome. The innate immune system acts as the first line of defense, sensing the virus through pattern recognition receptors and activating inflammatory pathways that promote viral clearance. Here, we discuss innate immune processes involved in SARS-CoV-2 recognition and the resultant inflammation. Improved understanding of how the innate immune system detects and responds to SARS-CoV-2 will help identify targeted therapeutic modalities that mitigate severe disease and improve patient outcomes.

Indexed as

Immunity, InnateAnimalsCOVID-19CytokinesHumansImmune EvasionInflammasomesNLR ProteinsReceptors, Pattern RecognitionSARS-CoV-2Signal TransductionToll-Like ReceptorsVirus InternalizationCytokinesInflammasomesNLR ProteinsReceptors, Pattern RecognitionToll-Like Receptors

Identifiers

PMID35105981
PMCPMC8935980

What OpenQuestion holds

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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.