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Record W3105314400 · doi:10.1093/femspd/ftaa068

Evaluation of the effect of T regulatory cell depletion and donor BCG vaccination on <i>Mycobacterium tuberculosis</i> H37Ra infection using an <i>in vitro</i> model of human PBMC infection

2020· article· en· W3105314400 on OpenAlexaff
Sudha Bhavanam, Gina R. Rayat, M. Keelan, Dennis Kunimoto, Steven J. Drews

Bibliographic record

VenuePathogens and Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsCanadian Blood ServicesUniversity of Alberta
Fundersnot available
KeywordsPeripheral blood mononuclear cellImmunologyImmune systemMycobacterium tuberculosisVaccinationMedicineTuberculosisCytokineRegulatory T cellIn vitroT cellBiologyIL-2 receptorPathology

Abstract

fetched live from OpenAlex

This study evaluated the effect of T regulatory cells (Treg cells) and the impact of BCG vaccination history of donors using an in vitro model of Mycobacterium tuberculosis H37Ra infection of peripheral blood mononuclear cells (PBMCs). PBMCs from donors with or without prior BCG vaccination were depleted of Treg cells (PBMCs-Tregs) or not depleted with Treg cells (PBMCs + Tregs) were infected up to 8 days with Mtb H37Ra. Cell aggregates were smaller in PBMCs-Tregs compared to PBMCs + Tregs at day 8 post-infection. Mtb CFUs were higher in the PBMCs-Tregs compared to PBMCs + Tregs at days 3, 5 and 8. The levels of IL-17, IFN-γ (at days 3 and 5), and TNF-α and IL-6 (at day 3) were lower in PBMCs-Tregs compared to PBMCs + Tregs. In contrast, the levels of IL-10 and IL-4 cytokines were higher at day 3 in PBMCs-Tregs compared to PBMCs + Tregs. BCG vaccination status of donors had no impact on the mycobacterial culture, level of cytokines and immune cell populations. This study shows that depletion of Tregs in human PBMCs infected with Mtb H37Ra in vitro leads to a shift from a Th1 to a Th2 cytokine rich environment that supports the survival of Mtb in this model.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.040
GPT teacher head0.317
Teacher spread0.277 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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