The Role of Complement in the Development and Manifestation of Murine Atherogenic Inflammation: Novel Avenues
Bibliographic record
Abstract
Atherosclerosis is a chronic progressive inflammatory disease which manifests in the arterial vascular tree. It is a major cause of cardiovascular morbidity and contributes significantly to mortality in the developed world. Triggers for this inflammatory process are elevated levels of cholesterol, bacterial infection and obesity. The immune response in atherosclerosis is essentially pro-atherogenic, leading to lipid accumulation and cellular changes within the arterial wall. Small-animal models of atherosclerosis are used to study the relevance of candidate factors (cells, genes, diets) in the development and progression of lesions. From a multidisciplinary viewpoint, there are challenges and limitations to this approach. Activation of complement determines or modifies the outcome of acute and chronic inflammation. This review dissects the role of complement in the early development as well as the progressive manifestation of murine atherosclerosis and the advances in knowledge provided by the use of specific mouse models. It gives a critical overview of existing models, analyses seemingly conflicting results obtained with complement-deficient mouse models, highlights the importance of interrelationships between pro-coagulpant activity, adipose tissue, macrophages and complement, and uncovers exciting avenues of topical research.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".