Macrophages in vascular disease: Roles of mitochondria and metabolic mechanisms
Bibliographic record
Abstract
Macrophages are a dynamic cell type of the immune system implicated in the pathophysiology of vascular diseases and are a major contributor to pathological inflammation. Excessive macrophage accumulation, activation, and polarization is observed in aortic aneurysm (AA), atherosclerosis, and pulmonary arterial hypertension. In general, macrophages become activated and polarized to a pro-inflammatory phenotype, which dramatically changes cell behavior to become pro-inflammatory and infiltrative. These cell types become cumbersome and fail to be cleared by normal mechanisms such as autophagy. The result is a hyper-inflammatory environment causing the recruitment of adjacent cells and circulating immune cells to further augment the inflammatory response. In AA, this leads to excessive ECM degradation and chemokine secretion, ultimately causing macrophages to dominate the immune cell landscape in the aortic wall. In atherosclerosis, monocytes are recruited to the vascular wall, where they polarize to the pro-inflammatory phenotype and induce inflammatory pathway activation. This leads to the development of foam cells, which significantly contribute to neointima and necrotic core formation in atherosclerotic plaques. Pro-inflammatory macrophages, which affect other vascular diseases, present with fragmented mitochondria and corresponding metabolic dysfunction. Targeting macrophage mitochondrial dynamics has proved to be an exciting potential therapeutic approach to combat vascular disease. This review will summarize mitochondrial and metabolic mechanisms of macrophage activation, polarization, and accumulation in vascular diseases.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".