Fatal attractions that trigger inflammation and drive atherosclerotic disease
Bibliographic record
Abstract
BACKGROUND: Atherosclerosis is the salient, underlying cause of cardiovascular diseases, such as arrhythmia, coronary artery disease, cardiomyopathy, pulmonary embolism and myocardial infarction. In recent years, atherosclerosis pathophysiology has evolved from a lipid-based to an inflammation-centric ideology. METHODS: This narrative review is comprised of review and original articles that were found through the PubMed search engine. The following search terms or amalgamation of terms were used: "cardiovascular disease," "atherosclerosis," "inflammation," "GRP78," "Hsp60," "oxidative low-density lipoproteins," "aldehyde dehydrogenase," "β2-glycoprotein," "lipoprotein lipase A," "human cytomegalovirus." "SARS-CoV-2," "chlamydia pneumonia," "autophagy," "thrombosis" and "therapeutics." RESULTS: Emerging evidence supports the concept that atherosclerosis is associated with the interaction between cell surface expression of stress response chaperones, including GRP78 and Hsp60, and their respective autoantibodies. Moreover, various other autoantigens and their autoantibodies have displayed a compelling connection with the development of atherosclerosis, including oxidative low-density lipoproteins, aldehyde dehydrogenase, β2-glycoprotein and lipoprotein lipase A. Atherosclerosis progression is also concurrent with viral and bacterial activators of various diseases. This narrative review will focus on the contributions of human cytomegalovirus as well as SARS-CoV-2 and chlamydia pneumonia in atherosclerosis development. Notably, the interaction of an autoantigen with their respective autoantibodies or the presence of a foreign antigen can enhance inflammation development, which leads to atherosclerotic lesion progression. CONCLUSION: We will highlight and discuss the complex role of the interaction between autoantigens and autoantibodies, and the presence of foreign antigens in the development of atherosclerotic lesions in relationship to pro-inflammatory responses.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".