Total particulate matter concentration skews cigarette smoke's gene expression profile
Bibliographic record
Abstract
Exposure of small animals to cigarette smoke is widely used as a model to study the pathogenesis of chronic obstructive pulmonary disease. However, protocols and exposure systems utilised vary substantially and it is unclear how these different systems compare. We analysed the gene expression profile of six publically available murine datasets from different cigarette smoke-exposure systems and related the gene signatures to three clinical cohorts. 234 genes significantly regulated by cigarette smoke in at least one model were used to construct a 55-gene network containing 17 clusters. Increasing numbers of differentially regulated clusters were associated with higher total particulate matter concentrations in the different datasets. Low total particulate matter-induced genes mainly related to xenobiotic/detoxification responses, while higher total particulate matter activated immune/inflammatory processes in addition to xenobiotic/detoxification responses. To translate these observations to the clinic, we analysed the regulation of the revealed network in three human cohorts. Similar to mice, we observed marked differences in the number of regulated clusters between the cohorts. These differences were not determined by pack-year. Although none of the experimental models exhibited a complete alignment with any of the human cohorts, some exposure systems showed higher resemblance. Thus, depending on the cohort, clinically observed changes in gene expression may be mirrored more closely by specific cigarette smoke exposure systems. This study emphasises the need for careful validation of animal models.
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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 | 0.001 | 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".