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Cited by 751

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

The immunology and immunopathology of COVID-19

Open access
Merad, Miriam, Blish, Catherine A., Sallusto, Federica, Iwasaki, Akiko·2022·Science

Abstract

Considerable research effort has been made worldwide to decipher the immune response triggered upon severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, identify the drivers of severe and fatal COVID-19, and understand what leads to the prolongation of symptoms after disease resolution. We review the results of almost 2 years of COVID-19 immunology research and discuss definitive findings and remaining questions regarding our understanding of COVID-19 pathophysiology. We discuss emerging understanding of differences in immune responses seen in those with and without Long Covid syndrome, also known as post-acute sequelae of SARS-CoV-2. We hope that the knowledge gained from this COVID-19 research will be applied in studies of inflammatory processes involved in critical and chronic illnesses, which remain a major unmet need.

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Registry

Crossref · 10.1126/science.abm8108

Cites

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Metadata

Authors
Merad, M., Blish, C. A., Sallusto, F., & Iwasaki, A.
Venue
Science
Published
2022-03-11
Volume
375
Issue
6585
Pages
1122-1127
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W4221026046
Cited by
751
References
23
FWCI
81.66
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The immunology and immunopathology of COVID-19. https://app.cochat.ai/papers/10.1126/science.abm8108
The paper
Merad, M., Blish, C. A., Sallusto, F., & Iwasaki, A. (2022). The immunology and immunopathology of COVID-19. Science. https://doi.org/10.1126/science.abm8108

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.