Evidence map›Paper›PMID 39770368›Full record

ReviewPathogens (Basel, Switzerland)2024

Detrimental Effects of Anti-Nucleocapsid Antibodies in SARS-CoV-2 Infection, Reinfection, and the Post-Acute Sequelae of COVID-19.

Emi E Nakayama, Tatsuo Shioda

Abstract readReview
In one paragraph

Review in Pathogens (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
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.

Emi E NakayamaDepartment of Viral Infections, Research Institute for Microbial Diseases, Osaka University, Suita 565-0871, Japan.ORCID 0000-0002-0477-0980
Tatsuo ShiodaDepartment of Viral Infections, Research Institute for Microbial Diseases, Osaka University, Suita 565-0871, Japan.ORCID 0000-0002-3439-8331

Funding

Center for Infectious Disease Education and Research, CiDER JM00000160
6 · The paper itself

Abstract

Antibody-dependent enhancement (ADE) is a phenomenon in which antibodies enhance subsequent viral infections rather than preventing them. Sub-optimal levels of neutralizing antibodies in individuals infected with dengue virus are known to be associated with severe disease upon reinfection with a different dengue virus serotype. For Severe Acute Respiratory Syndrome Coronavirus type-2 infection, three types of ADE have been proposed: (1) Fc receptor-dependent ADE of infection in cells expressing Fc receptors, such as macrophages by anti-spike antibodies, (2) Fc receptor-independent ADE of infection in epithelial cells by anti-spike antibodies, and (3) Fc receptor-dependent ADE of cytokine production in cells expressing Fc receptors, such as macrophages by anti-nucleocapsid antibodies. This review focuses on the Fc receptor-dependent ADE of cytokine production induced by anti-nucleocapsid antibodies, examining its potential role in severe COVID-19 during reinfection and its contribution to the post-acute sequelae of COVID-19, i.e., prolonged symptoms lasting at least three months after the acute phase of the disease. We also discuss the protective effects of recently identified anti-spike antibodies that neutralize Omicron variants.

Indexed as

Antibodies, NeutralizingAntibodies, ViralAntibody-Dependent EnhancementCOVID-19ReinfectionSARS-CoV-2CytokinesHumansNucleocapsidPost-Acute COVID-19 SyndromeReceptors, FcAntibodies, NeutralizingAntibodies, ViralCytokinesReceptors, Fcantibody-dependent enhancementbroadly neutralizing antibodyCOVID-19long-COVIDnucleocapsidpost-acute COVID-19 syndrome

Identifiers

PMID39770368
PMCPMC11728538

What OpenQuestion holds

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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.