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Record W3154015170 · doi:10.1128/mbio.00226-21

<i>In Vivo</i> Antigen Expression Regulates CD4 T Cell Differentiation and Vaccine Efficacy against Mycobacterium tuberculosis Infection

2021· article· en· W3154015170 on OpenAlexafffund
Helena Strand Clemmensen, Jean-Yves Dubé, Fiona McIntosh, Ida Rosenkrands, Gregers Jungersen, Claus Aagaard, Peter Andersen, Marcel A. Behr, Rasmus Mortensen

Bibliographic record

VenuemBio · 2021
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Institute of Allergy and Infectious DiseasesStatens Serum InstitutCanadian Institutes of Health ResearchMcGill University Health CentreNational Institutes of HealthH. Lundbeck A/SLundbeckfondenGovernment of CanadaMcGill UniversitySundhed og Sygdom, Det Frie Forskningsråd
KeywordsMycobacterium tuberculosisAntigenTuberculosisIn vivoImmunologyVirologyBiologyMicrobiologyTuberculosis vaccinesMedicineGenetics

Abstract

fetched live from OpenAlex

Tuberculosis, caused by Mtb, constitutes a global health crisis of massive proportions and the impact of the current coronavirus disease 2019 (COVID-19) pandemic is expected to cause a rise in tuberculosis-related deaths. Improved vaccines are therefore needed more than ever, but a lack of knowledge on protective immunity hampers their development. The present study shows that constitutively expressed antigens with high availability drive highly differentiated CD4 T cells with diminished protective capacity, which could be a survival strategy by Mtb to evade T cell immunity against key antigens. We demonstrate that immunization with such antigens can counteract this phenomenon by maintaining antigen-specific T cells in a state of low differentiation. Future vaccine strategies should therefore explore combinations of multiple highly expressed antigens and we suggest that T cell differentiation could be used as a readily measurable parameter to identify these in both preclinical and clinical studies.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.012
GPT teacher head0.277
Teacher spread0.264 · 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 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

Citations16
Published2021
Admission routes2
Has abstractyes

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