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Record W2625830429 · doi:10.1080/21645515.2017.1337616

An adjuvant-modulated vaccine response in human whole blood

2017· article· en· W2625830429 on OpenAlexaff
Jalil Hakimi, Ali Azizi, Salvador F. Ausar, Stephen Todryk, Nausheen Rahman, Roger H. Brookes

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsSanofi (Canada)
Fundersnot available
KeywordsAdjuvantImmune systemImmunologyVaccinationAntigenIn vivoMedicineVaccine efficacyBiologyBiotechnology

Abstract

fetched live from OpenAlex

The restimulation of an immune memory response by in vitro culture of blood cells with a specific antigen has been used as a way to gauge immunity to vaccines for decades. In this commentary we discuss a less appreciated application to support vaccine process development. We report that human whole blood from pre-primed subjects can generate a profound adjuvant-modulated, antigen-specific response to several different vaccine formulations. The response is able to differentiate subtle changes in the quality of an immune memory response to vaccine formulations and can be used to select optimal conditions relating to a particular manufacture process step. While questions relating to closeness to in vivo vaccination remain, the approach is another big step nearer to the more relevant human response. It has special importance for new adjuvant development, complementing other preclinical in vivo and in vitro approaches to considerably de-risk progression of novel vaccines before and throughout early clinical development. Broader implications of the approach are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.001

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 head0.024
GPT teacher head0.313
Teacher spread0.289 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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