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Record W2887902144 · doi:10.3791/57554

A Suction Blister Protocol to Study Human T-cell Recall Responses <em>In Vivo</em>

2018· article· en· W2887902144 on OpenAlexaff
Line Lindebo Holm, Milica Vukmanovic‐Stejic, Thomas Blauenfeldt, Thomas Benfield, Peter Andersen, Arne N. Akbar, Morten Rühwald

Bibliographic record

VenueJournal of Visualized Experiments · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsInstitute of Infection and Immunity
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsImmunologyAntigenImmune systemMycobacterium tuberculosisAntigen presentationContext (archaeology)In vivoT cellMedicineBiologyAntigen-presenting cellTuberculosisPathology

Abstract

fetched live from OpenAlex

Cutaneous antigen-recall models allow for studies of human memory responses in vivo. When combined with skin suction blister (SB) induction, this model offers accessibility to rare populations of antigen-specific T-cells representative of the cellular memory response as well as the cytokine microenvironment in situ. This report describes the practical procedure of a cutaneous recall, an SB induction, and a harvest of antigen-specific T-cells. To exemplify the method, the tuberculin skin test is used for antigenic recall in individuals who, prior to this study, underwent a Bacillus Calmette-Guérin vaccination against an infection with Mycobacterium tuberculosis. Finally, examples of multiplex and flow cytometric analyses of SB specimens are provided, illustrating high fractions of antigen-specific polyfunctional CD4+ T-cells available by this sampling method compared with cells isolated from the blood. The method described here is safe and minimally invasive, provides a unique opportunity to study both innate and adaptive immune responses in vivo, and may be beneficial to a broad community of researchers working with cell-mediated immunity and human memory responses, in the context of vaccine development.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.412
Teacher spread0.375 · 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
Published2018
Admission routes1
Has abstractyes

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