Effects of mild prenatal smoke exposure on T cell development in murine offspring
Bibliographic record
Abstract
Introduction: Epidemiological studies demonstrate that in utero cigarette smoke exposure negatively affects lung development, thereby increasing risks for asthma and COPD (Svanes, Thorax 2010). As underreporting of smoking is frequent in pregnant women, mild smoking might not be reported. In the present project, we asked if mild maternal smoking is sufficient to affect the development of offspring. Objectives: We developed a murine mild prenatal smoke exposure model and investigated body weight, lung function and immune development. Methods: Female C57BL/6 mice were exposed to mainstream smoke 4 days before mating and during gestation until birth for 60 min per day; ~6 cigarettes (research cigarettes 3R4F); inexpose exposure system (SCIREQ, Canada). T cells were analyzed by flow cytometry in lung, spleen and thymus. Lung function was assessed by FlexiVent system (SCIREQ, Canada). Results: In utero smoke-exposed offspring had normal body weight and lung function at baseline and after methacholine provocation. However, thymic CD3high T cell populations and CD4+ T cells were increased; CD8+ T cells were not affected; double-positive, double-negative (CD4/CD8) T cells and Treg (male) were decreased in in utero smoke-exposed offspring compared to air controls. Conclusion: Despite normal body weight and lung function, our data reveal altered T cell composition in the thymus in a mild maternal smoking model. This could be a first indication of disturbed T cell development in in utero smoke-exposed offspring. Further, this could lead to altered immune responses and a higher asthma risk observed in in utero cigarette smoke-exposed children. Detailed analyses of distinct precursor T cells are required.
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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 | 0.001 | 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) | 0.002 | 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".