Cathepsin G deficiency decreases complexity of atherosclerotic lesions in apolipoprotein E-deficient mice
Bibliographic record
Abstract
Cathepsin G is a serine protease with a broad range of catalytic activities, including production of angiotensin II, degradation of extracellular matrix and cell-cell junctions, modulation of chemotactic responses, and induction of apoptosis. Cathepsin G mRNA expression is increased in human coronary atheroma vs. the normal vessel. To assess whether cathepsin G modulates atherosclerosis, cathepsin G knockout (Cstg(-/-)) mice were bred with apolipoprotein E knockout (Apoe(-/-)) mice to obtain Ctsg(+/-)Apoe(-/-) and Ctsg(+/+)Apoe(-/-) mice. Heterozygous cathepsin G deficiency led to a 70% decrease in cathepsin G activity in bone marrow cells, but this reduced activity did not impair generation of angiotensin II in bone marrow-derived macrophages (BMDM). Atherosclerotic lesions were compared in male Cstg(+/-)Apoe(-/-) and Cstg(+/+)Apoe(-/-) mice after 8 wk on a high-fat diet. Plasma cholesterol levels and cholesterol distribution within serum lipoprotein fractions did not differ between genotypes nor did the atherosclerotic lesion areas in either the aortic root or aortic arch. Cstg(+/-)Apoe(-/-) mice, however, showed a lower percentage of complex lesions within the aortic root and a smaller number of apoptotic cells compared with Cstg(+/+)Apoe(-/-) littermates. Furthermore, apoptotic Cstg(-/-) BMDM were more efficiently engulfed by phagocytic BMDM than were apoptotic Ctsg(+/+) BMDM. Thus cathepsin G activity may impair efferocytosis, which could lead to an accumulation of lesion-associated apoptotic cells and the accelerated progression of early atherosclerotic lesions to more complex lesions in Apoe(-/-) mice.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".