Cigarette smoke sets up a pro-inflammatory circuit in the lung that induces the hyper-activation of autoreactive T helper cells
Bibliographic record
Abstract
Cigarette smoke (CS) exposure increases the risk of multiple sclerosis; however, the mechanisms are unclear. To investigate this, we exposed C57BL6/J mice to CS or ambient air (AA) for 8 weeks using a protocol modelling human chronic CS exposure and studied the impact of these exposures on the development of experimental autoimmune encephalomyelitis (EAE). We found that CS increased the incidence of neurological signs in 2D2 myelin oligodendrocyte glycoprotein (MOG) T cell receptor transgenic mice, whereas in active EAE induced by MOG peptide and Complete Freund's adjuvant, CS delayed the onset of disease. In both cases, the effects of CS tended to be greater in the males who also developed greater leukocyte infiltration and expression of IL-12p40 and other cytokines. To gain insights into the paradoxical effects of CS in these EAE models, we transferred congenically-marked pMOG-reactive T helper cells into AA- or CS-exposed mice and examined the phenotype of these cells in the lungs, spleen, and spinal cord. We found that CS-exposed lungs acted as a sink for the pMOG-reactive T cells, in that more of these T cells homed to the lungs instead of the spinal cord. However, these CS-educated pMOG Th cells exhibited a hyperactivated Th phenotype with greater expression of activation markers, IL-17, and GM-CSF, a cytokine that is essential for EAE development. Intranasal treatment with anti-IL-12p40 negated these effects of CS on the expression of pro-inflammatory cytokines by pMOG Th cells. These studies suggest that CS sets up a pro-inflammatory circuit in the lung that amplifies the encephalitogenic properties of myelin-specific T effector cells.
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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) | 0.001 | 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".