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Record W4282822749 · doi:10.1016/j.celrep.2022.111013

Temporal associations of B and T cell immunity with robust vaccine responsiveness in a 16-week interval BNT162b2 regimen

2022· article· en· W4282822749 on OpenAlexafffund
Manon Nayrac, Mathieu Dubé, Gérémy Sannier, Alexandre Nicolas, Lorie Marchitto, Olivier Tastet, Alexandra Tauzin, Nathalie Brassard, Raphaël Lima-Barbosa, Guillaume Beaudoin-Bussières, Dani Vézina, Shang Yu Gong, Mehdi Benlarbi, Romain Gasser, Annemarie Laumaea, Jérémie Prévost, Catherine Bourassa, Gabrielle Gendron‐Lepage, Halima Medjahed, Guillaume Goyette, Gloria-Gabrielle Ortega-Delgado, Mélanie Laporte, Julia Niessl, Laurie Gokool, Chantal Morrisseau, Pascale Arlotto, Jonathan Richard, Justin Bélair, Alexandre Prat, Cécile Tremblay, Valérie Martel‐Laferrière, Andrés Finzi, Daniel E. Kaufmann

Bibliographic record

VenueCell Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcGill University Health CentreUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - SantéUniversité de MontréalFondation du CHUMCanada Foundation for InnovationCanadian Institutes of Health ResearchMitacsCanada Research Chairs
KeywordsImmune systemImmunologyT cellBoosting (machine learning)CD8RegimenMedicineCytotoxic T cellCellRecallComputational biologyBioinformaticsBiologyInternal medicineComputer sciencePsychologyGeneticsMachine learningIn vitro

Abstract

fetched live from OpenAlex

T cell responses further compared with the first dose, unsupervised clustering of single-cell features reveals phenotypic and functional shifts over time and between cohorts. Integrated analysis shows longitudinal immune component-specific associations, with early T helper responses post first dose correlating with B cell responses after the second dose, and memory T helper generated between doses correlating with CD8 T cell responses after boosting. Therefore, boosting elicits a robust cellular recall response after the 16-week interval, indicating functional immune memory.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.315
Teacher spread0.270 · 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.

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

Citations22
Published2022
Admission routes2
Has abstractyes

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