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Record W3092025944 · doi:10.1101/2020.10.09.333278

Major role of IgM in the neutralizing activity of convalescent plasma against SARS-CoV-2

2020· preprint· en· W3092025944 on OpenAlexafffund
Romain Gasser, Marc Cloutier, Jérémie Prévost, Corby Fink, Éric Ducas, Shilei Ding, Nathalie Dussault, Patricia Landry, Tony Tremblay, Audrey Laforce‐Lavoie, Antoine Lewin, Guillaume Beaudoin-Bussières, Annemarie Laumaea, Halima Medjahed, Catherine Larochelle, Jonathan Richard, Gregory A. Dekaban, Jimmy D. Dikeakos, Renée Bazin, Andrés Finzi

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversité de SherbrookeWestern UniversityUniversité de MontréalRobarts Clinical TrialsHéma-QuébecMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health ResearchFondation du CHUM
KeywordsAntibodyNeutralizationIsotypeConvalescent plasmaImmunologyCoronavirus disease 2019 (COVID-19)Neutralizing antibodyVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Immunoglobulin GMedicineVirusImmunoglobulin MMonoclonal antibodyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Characterization of the humoral response to SARS-CoV-2, the etiological agent of Covid-19, is essential to help control the infection. In this regard, we and others recently reported that the neutralization activity of plasma from COVID-19 patients decreases rapidly during the first weeks after recovery. However, the specific role of each immunoglobulin isotype in the overall neutralizing capacity is still not well understood. In this study, we selected plasma from a cohort of Covid-19 convalescent patients and selectively depleted immunoglobulin A, M or G before testing the remaining neutralizing capacity of the depleted plasma. We found that depletion of immunoglobulin M was associated with the most substantial loss of virus neutralization, followed by immunoglobulin G. This observation may help design efficient antibody-based COVID-19 therapies and may also explain the increased susceptibility to SARS-CoV-2 of autoimmune patients receiving therapies that impair the production of IgM.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.299
Teacher spread0.255 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207