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Record W4212912712 · doi:10.1128/spectrum.02262-21

SARS-CoV-2 Virus-Like Particle Neutralizing Capacity in Blood Donors Depends on Serological Profile and Donor-Declared SARS-CoV-2 Vaccination History

2022· article· en· W4212912712 on OpenAlexafffundabout
Steven J. Drews, Queenie Hu, Reuben Samson, Kento T. Abe, Bhavisha Rathod, Karen Colwill, Anne‐Claude Gingras, Qilong Yi, Sheila F. O’Brien

Bibliographic record

VenueMicrobiology Spectrum · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of OttawaUniversity of TorontoCanadian Blood ServicesLunenfeld-Tanenbaum Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta InnovatesGovernment of Canada
KeywordsSerologyVaccinationVirologyTiterNeutralizing antibodyMedicinePopulationAntibodyVirusNeutralizationImmunologyEnvironmental health

Abstract

fetched live from OpenAlex

In the first 3 months of 2021 as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination was in the initial stages of a mass rollout, Canadian blood donors had various levels of humoral protection against wild-type and variant of concern (VOC) SARS-CoV-2. Very few Canadians would have received a second dose of a SARS-CoV-2 vaccine. In this study, we identified elevated levels of neutralizing capacity, albeit with reduced neutralization capacity against one or more SARS-CoV-2 strains (wild type and VOCs) in vaccinated blood donors. This broad neutralizing response we present regardless of evidence of natural SARS-CoV-2 infection. Neutralizing capacity against wild type and VOCs varied significantly within the unvaccinated group, with one subset of unvaccinated plasma specimens (unvaccinated and no anti-N) having no measurable wild type- nor variant-neutralizing capacity. The study is important because it indicates that vaccination can be associated with a broad neutralizing antibody capacity of donor plasma against SARS-CoV-2 VOCs.

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 score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.071
GPT teacher head0.303
Teacher spread0.233 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

Explore more

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