The role of the CCR1 receptor in the inflammatory response to tobacco smoke in a mouse model
Bibliographic record
Abstract
OBJECTIVE: The aim was to create pathological changes in mice relevant to human smoke exposure that can be used to further understand the mechanisms and pathology of smoke-induced inflammatory disease. METHODS: Mice were exposed to tobacco smoke or lipopolysaccharide (LPS) to generate an inflammatory infiltrate within the lungs. RESULTS: Tobacco smoke exposure over a 4 day period led to neutrophilia in the lungs of BALB/c mice. Within the inflammatory exudates, significant changes were also seen in protein levels of IL-1B, IL-6, MIP-2, KC (IL-8) and TIMP-1 as measured by ELISA. Further protein changes, as measured via multiplex analysis revealed increased levels of MMP-9, MDC, LIF and MCP-1, amongst other mediators. Major changes in whole lung tissue gene expression patterns were observed. The neutrophilia seen after smoke exposure was steroid-insensitive, relative to doses of steroid needed to reduce LPS-driven neutrophilia in controls. This exposes pathological switches that are changed upon exposure to tobacco smoke, rendering steroids less effective under these conditions. Challenge of chemokine receptor type 1 (CCR1) KO mice in the tobacco smoke model showed that lack of this gene protected the mice from smoke-induced inflammation. CONCLUSIONS: This suggests the CCR1 receptor has a key role in the pathogenesis of smoke-induced inflammation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".