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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".