214 How do n3-polyunsaturated fatty acids reduce vascular endothelial dysfunction due to inflammation? a literature review
Bibliographic record
Abstract
Background For more than 50 years, there has been an ongoing debate about whether long chain n-3 polyunsaturated fatty acids can be used as therapeutic agents in cardiovascular disease (CVD). Interest in the association between n3-PUFAs and CVD grew when epidemiological studies suggested that within the Greenland Inuit, there is a connection between a high intake of n3-PUFAs and reduced incidence of CVD. Objective The objective of this literature review is to look at literature out there that investigates the correlation between consumption of n3-PUFAs and inflammation. Methodology A literature review was carried out to analyse the effects of an increased intake of n3-PUFAs on vascular endothelial dysfunction. PubMed and SCOPUS were used to search for relevant articles, and abstracts were screened using inclusion criteria of the terms: vascular endothelial dysfunction, inflammation, n3-polyunsaturated fatty acids, polyunsaturated fatty acids, cardiovascular disease. A total of 31 studies were included in the review. Results and discussion: The findings suggest an inversely proportional relationship between n3-PUFA intake and vascular endothelial dysfunction. N3-PUFAs affect the homeostatic production of vasoconstrictive and vasodilatory factors in numerous ways: reducing the production of pro-inflammatory eicosanoids, modulating the expression of certain inflammatory genes, affecting leucocyte chemotaxis and more. Endothelial dysfunction occurs when a damaged endothelium interferes with the balanced production of endothelial factors; hence n3-PUFAs have the potential to play a big role in reducing inflammation and, ultimately endothelial dysfunction. Conclusion By reducing inflammation through the various ways discussed, n3-PUFAs can reduce the incidence of the inflammatory pathway and, hence, decrease the incidence of cardiovascular disease, as seen in the Greenland Eskimo population over half a century ago. Conflict of Interest N/A
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".