Urban Air Pollution Particulates Suppress Human T-Cell Responses to Mycobacterium Tuberculosis
Bibliographic record
Abstract
Tuberculosis (TB) and air pollution both contribute significantly to the global burden of disease. Epidemiological studies show that exposure to household and urban air pollution increase the risk of new infections with Mycobacterium tuberculosis (M.tb) and the development of TB in persons infected with M.tb and alter treatment outcomes. There is increasing evidence that particulate matter (PM) exposure weakens protective antimycobacterial host immunity. Mechanisms by which exposure to urban PM may adversely affect M.tb-specific human T cell functions have not been studied. We, therefore, explored the effects of urban air pollution PM2.5 (aerodynamic diameters ≤2.5µm) on M.tb-specific T cell functions in human peripheral blood mononuclear cells (PBMC). PM2.5 exposure decreased the capacity of PBMC to control the growth of M.tb and the M.tb-induced expression of CD69, an early surface activation marker expressed on CD3+ T cells. PM2.5 exposure also decreased the production of IFN-γ in CD3+, TNF-α in CD3+ and CD14+ M.tb-infected PBMC, and the M.tb-induced expression of T-box transcription factor TBX21 (T-bet). In contrast, PM2.5 exposure increased the expression of anti-inflammatory cytokine IL-10 in CD3+ and CD14+ PBMC. Taken together, PM2.5 exposure of PBMC prior to infection with M.tb impairs critical antimycobacterial T cell immune functions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".