Continuous and Discontinuous Cigarette Smoke Exposure Differentially Affects Protective Th1 Immunity against Pulmonary Tuberculosis
Bibliographic record
Abstract
Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis, is the leading cause of death due to a bacterial pathogen. Emerging epidemiologic evidence suggests that the leading risk factor associated with TB mortality is cigarette smoke exposure. Despite this, it remains poorly understood what is the effect of cigarette smoke exposure on anti-TB immunity and whether its potential detrimental effect can be reversed by cigarette smoking cessation. In our current study, we have investigated the impact of both continuous and discontinuous cigarette smoke exposure on the development of anti-mycobacterial type 1 immunity in murine models. We find that while continuous cigarette smoke exposure severely impairs type 1 immunity in the lung, a short-term smoking cessation allows rapid restoration of anti-mycobacterial immunity. The ability of continuous cigarette smoke exposure to dampen type 1 protective immunity is attributed locally to its affects on innate immune cells in the lung. Continuous cigarette smoke exposure locally, by not systemically, impairs APC accumulation and their production of TNF, IL-12, and RANTES, blunts the recruitment of CD4+IFN-γ+ T cells to the lung, and weakens the formation of granuloma. On the other hand, smoking cessation was found to help restore type 1 immunity by rapidly improving the functionality of lung APCs, enhancing the recruitment of CD4+IFN-γ+ T cells to the lung, and promoting the formation of granuloma. Our study for the first time demonstrates that continuous, but not discontinuous, cigarette smoke exposure severely impedes the lung expression of anti-TB Th1 immunity via inhibiting innate immune activation and lung T cell recruitment. Our findings thus suggest cigarette smoking cessation to be beneficial to the control of pulmonary TB.
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 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".