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Record W3092801527 · doi:10.1128/mbio.02590-20

Decline of Humoral Responses against SARS-CoV-2 Spike in Convalescent Individuals

2020· article· en· W3092801527 on OpenAlexafffund
Guillaume Beaudoin-Bussières, Annemarie Laumaea, Sai Priya Anand, Jérémie Prévost, Romain Gasser, Guillaume Goyette, Halima Medjahed, Josée Perreault, Tony Tremblay, Antoine Lewin, Laurie Gokool, Chantal Morrisseau, Philippe Bégin, Cécile Tremblay, Valérie Martel‐Laferrière, Daniel E. Kaufmann, Jonathan Richard, Renée Bazin, Andrés Finzi

Bibliographic record

VenuemBio · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcGill UniversityHéma-QuébecUniversité de Montréal
FundersU.S. Military HIV Research ProgramFonds de Recherche du Québec - SantéamfAR, The Foundation for AIDS ResearchUniversity of OttawaCanadian Institutes of Health ResearchMitacsGovernment of CanadaFondation du CHUM
KeywordsConvalescent plasmaMedicineNeutralizationAntibodyImmunologyClinical trialTiterNeutralizing antibodyVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)CoronavirusDiseaseInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

While waiting for an efficient vaccine to protect against SARS-CoV-2 infection, alternative approaches to treat or prevent acute COVID-19 are urgently needed. Transfusion of convalescent plasma to treat COVID-19 patients is currently being explored; neutralizing activity in convalescent plasma is thought to play a central role in the efficacy of this treatment. Here, we observed that plasma neutralization activity decreased a few weeks after the onset of the symptoms. If neutralizing activity is required for the efficacy of convalescent plasma transfer, our results suggest that convalescent plasma should be recovered rapidly after the donor recovers from active infection.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.112
GPT teacher head0.388
Teacher spread0.276 · 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 designObservational
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

Citations215
Published2020
Admission routes2
Has abstractyes

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