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Record W4400588973 · doi:10.1038/s41590-024-01897-8

Specific CD4+ T cell phenotypes associate with bacterial control in people who ‘resist’ infection with Mycobacterium tuberculosis

2024· article· en· W4400588973 on OpenAlexaff
Meng Sun, Jolie M. Phan, Nathan S. Kieswetter, Huang Huang, Krystle K. Q. Yu, Malisa T. Smith, Yiran E. Liu, Chuanqi Wang, Sanjana Gupta, Gerlinde Obermoser, Holden T. Maecker, Akshaya Krishnan, Sundari Suresh, Neha Gupta, Mary Rieck, Péter Ács, Mustafa Ghanizada, Shin‐Heng Chiou, Purvesh Khatri, W. Henry Boom, Thomas R. Hawn, Catherine M. Stein, Harriet Mayanja‐Kizza, Mark M. Davis, Chetan Seshadri

Bibliographic record

VenueNature Immunology · 2024
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates Foundation
KeywordsMycobacterium tuberculosisPhenotypeTuberculosisBiologyMicrobiologyImmunologyVirologyGeneticsMedicineGenePathology

Abstract

fetched live from OpenAlex

Abstract A subset of individuals exposed to Mycobacterium tuberculosis ( Mtb ) that we refer to as ‘resisters’ (RSTR) show evidence of IFN-γ − T cell responses to Mtb -specific antigens despite serially negative results on clinical testing. Here we found that Mtb -specific T cells in RSTR were clonally expanded, confirming the priming of adaptive immune responses following Mtb exposure. RSTR CD4 + T cells showed enrichment of T H 17 and regulatory T cell-like functional programs compared to Mtb -specific T cells from individuals with latent Mtb infection. Using public datasets, we showed that these T H 17 cell-like functional programs were associated with lack of progression to active tuberculosis among South African adolescents with latent Mtb infection and with bacterial control in nonhuman primates. Our findings suggested that RSTR may successfully control Mtb following exposure and immune priming and established a set of T cell biomarkers to facilitate further study of this clinical phenotype.

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 score0.414
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.005
GPT teacher head0.252
Teacher spread0.247 · 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

Citations48
Published2024
Admission routes1
Has abstractyes

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