Evidence for a vascular microbiome and its role in vessel health and disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: We have summarized available evidence for and against the presence of a vascular microbiome. Studies that have attempted to detect bacteria and viruses in blood vessels in both health and disease are critiqued in an attempt to explain contrary results that may be due to variations in methodology. RECENT FINDINGS: Many studies have demonstrated the presence of both bacteria and viruses within diseased blood vessels. Evidence is most compelling in atherosclerosis; however, recent reports have raised questions about the potential role of microbes in nonatherosclerotic aortic aneurysms and vasculitis. Preliminary evidence also suggests that apparently normal vessels may harbor microbes. With the exception of certain viral infections (e.g. hepatitis C virus, HIV, Epstein-Barr virus, and cytomegalovirus) and infectious endocarditis, systemic vasculitides have not been convincingly associated with infectious agents. However, emerging data suggest that different communities of microbes may be present in noninflammatory and inflammatory large-vessel diseases. Whether variations in vascular microbial communities are the cause or a secondary result (epiphenomena) of vessel injury remains to be determined. SUMMARY: Blood vessels may not be sterile. Future studies of microbes in vessel health and disease may provide important insights into disease pathogenesis and suggest new therapies for diseases now considered to be idiopathic and refractory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".