Pharmacological and genetic reappraisals of protease and oxidative stress pathways in a mouse model of obstructive lung diseases
Bibliographic record
Abstract
Protease-antiprotease imbalance and oxidative stress are considered to be major pathophysiological hallmarks of severe obstructive lung diseases including chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF), but limited information is available on their direct roles in the regulation of pulmonary phenotypes. Here, we utilized βENaC-transgenic (Tg) mice, the previously established mouse model of severe obstructive lung diseases, to produce lower-mortality but pathophysiologically highly useful mouse model by backcrossing the original line with C57/BL6J mice. C57/BL6J-βENaC-Tg mice showed higher survival rates and key pulmonary abnormalities of COPD/CF, including mucous hypersecretion, inflammatory and emphysematous phenotypes and pulmonary dysfunction. DNA microarray analysis confirmed that protease- and oxidative stress-dependent pathways are activated in the lung tissue of C57/BL6J-βENaC-Tg mice. Treatments of C57/BL6J-βENaC-Tg mice with a serine protease inhibitor ONO-3403, a derivative of camostat methylate (CM), but not CM, and with an anti-oxidant N-acetylcystein significantly improved pulmonary emphysema and dysfunction. Moreover, depletion of a murine endogenous antioxidant vitamin C (VC), by genetic disruption of VC-synthesizing enzyme SMP30 in C57/BL6J-βENaC-Tg mice, exaggerated pulmonary phenotypes. Thus, these assessments clarified that protease-antiprotease imbalance and oxidative stress are critical pathways that exacerbate the pulmonary phenotypes of C57/BL6J-βENaC-Tg mice, consistent with the characteristics of human COPD/CF.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".