Expression of S100A8 correlates with inflammatory lung disease in congenic mice deficient of the cystic fibrosis transmembrane conductance regulator
Bibliographic record
Abstract
BACKGROUND: Lung disease in cystic fibrosis (CF) patients is dominated by chronic inflammation with an early and inappropriate influx of neutrophils causing airway destruction. Congenic C57BL/6 CF mice develop lung inflammatory disease similar to that of patients. In contrast, lungs of congenic BALB/c CF mice remain unaffected. The basis of the neutrophil influx to the airways of CF patients and C57BL/6 mice, and its precipitating factor(s) (spontaneous or infection induced) remains unclear. METHODS: The lungs of 20-day old congenic C57BL/6 (before any overt signs of inflammation) and BALB/c CF mouse lines maintained in sterile environments were investigated for distinctions in the neutrophil chemokines S100A8 and S100A9 by quantitative RT-PCR and RNA in situ hybridization, that were then correlated to neutrophil numbers. RESULTS: The lungs of C57BL/6 CF mice had spontaneous and significant elevation of both neutrophil chemokines S100A8 and S100A9 and a corresponding increase in neutrophils, in the absence of detectable pathogens. In contrast, BALB/c CF mouse lungs maintained under identical conditions, had similar elevations of S100A9 expression and resident neutrophil numbers, but diverged in having normal levels of S100A8. CONCLUSION: The results indicate early and spontaneous lung inflammation in CF mice, whose progression corresponds to increased expression of both S100A8 and S100A9, but not S100A9 alone. Moreover, since both C57BL/6 and BALB/c CF lungs were maintained under identical conditions and had similar elevations in S100A9 and neutrophils, the higher S100A8 expression in the former (or suppression in latter) is a result of secondary genetic influences rather than environment or differential infection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".