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Record W4220692154 · doi:10.1093/infdis/jiac105

Autoantibodies and COVID-19: 
Rediscovering Nonspecific Polyclonal B-Cell Activation?

2022· letter· en· W4220692154 on OpenAlexaff
Nevio Cimolai

Bibliographic record

VenueThe Journal of Infectious Diseases · 2022
Typeletter
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAutoantibodyPolyclonal antibodiesCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyImmunologyMedicineAntibodyPathologyDiseaseOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

To the Editor—In the search to explain advanced, prolonged, or post-coronavirus disease 2019 (COVID-19) disease, several investigative groups, including that of Acosta-Ampudia et al, have found presumed autoantibody activities, which at times have been linked to accentuated proinflammatory states or possible autoimmunity [1]. The mere association of coincident elevations of such antibodies or their intermediate-term persistence have tempted several hypotheses of pathogenesis, whether for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection directly or concomitant immunologically based phenomena. While it is certainly appropriate to focus on these findings as being coronavirus specific, are we only rediscovering nonspecific polyclonal B-cell activation? The concept of nonspecific polyclonal B-cell activation and, hence, nonspecific antibody production, has been well known for nearly a half century [2–4]. Whether for acute or persistent infections, a wide variety of autoantibodies, usually immunoglobulin G (IgG) subsets having low specificity, may develop to bind nonspecific antimicrobial or self-antigens. Among the latter are autoantibodies to cytokines, classic connective tissue disease autoantigens, and many other but variable host targets. Most of these nonspecific responses either markedly diminish or disappear over time. Such timing may span weeks to well over 1 year. The transient development of autoantibodies such as rheumatoid factor or cold agglutinins during acute Mycoplasma pneumoniae infections remains a classic example of such nonspecificity [5]. That a similar antibody production would arise with coronaviruses was heralded in the study of murine hepatitis virus [6]. Even more recent studies illustrate how such nonspecific events can follow many non–SARS-CoV-2 infections or vaccination ([7], Feng et al, unpublished). What has not been resolved, however, is whether these immune activations represent purposeful innate immunity tactics or aberrations supporting the microbial pathogen’s disease process or, perhaps, neither. Yet unresolved is the extent, if any, that such a B-cell cascade truly induces an autoantibody with definitive and/or long-lasting immune dysfunction. Theories of detrimental postinfection immunopathology abound for COVID-19. For example, common thrombotic events during infection, or rare ones after some vaccinations, have largely stimulated reconsideration of infection-associated antiphospholipid antibodies [8]. In the short term, and with the desire to better understand the complexity of an impactful pandemic, it is more common to assume that COVID-19–associated autoantibody production should somehow factor into either early and/or late disease. We must concede, however, that a mere attribution of autoantibody existence to functional autoimmune disease, including interinfection cytokine storm or advanced inflammatory states, may just be a rudimentary and/or preliminary association, as the nonspecific polyclonal B-cell activations that we have experienced with other infections and for which we continue to seek answers. For some patients, the finding of autoantibodies during active COVID-19 has the potential to bias the treating physician towards an assumption that the two are pathologically linked and, hence, may potentially elicit intervention. Just as unproven antiviral treatments may jeopardize patient status, so too may the assumptions that any such autoantibody may truly have a role in the disease course. Potential conflicts of interest. The ­author certifies no potential conflicts of interest. The author has submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0340.018
Insufficient payload (model declined to judge)0.0070.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.329
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes1
Has abstractno

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