Can N-3 polyunsaturated fatty acids be considered a potential adjuvant therapy for COVID-19-associated cardiovascular complications?
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has currently led to a global pandemic with millions of confirmed and increasing cases around the world. The novel SARS-CoV-2 not only affects the lungs causing severe acute respiratory dysfunction but also leads to significant dysfunction in multiple organs and physiological systems including the cardiovascular system. A plethora of studies have shown the viral infection triggers an exaggerated immune response, hypercoagulation and oxidative stress, which contribute significantly to poor cardiovascular outcomes observed in COVID-19 patients. To date, there are no approved vaccines or therapies for COVID-19. Accordingly, cardiovascular protective and supportive therapies are urgent and necessary to the overall prognosis of COVID-19 patients. Accumulating literature has demonstrated the beneficial effects of n-3 polyunsaturated fatty acids (n-3 PUFA) toward the cardiovascular system, which include ameliorating uncontrolled inflammatory reactions, reduced oxidative stress and mitigating coagulopathy. Moreover, it has been demonstrated the n-3 PUFAs, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are precursors to a group of potent bioactive lipid mediators, generated endogenously, which mediate many of the beneficial effects attributed to their parent compounds. Considering the favorable safety profile for n-3 PUFAs and their metabolites, it is reasonable to consider n-3 PUFAs as potential adjuvant therapies for the clinical management of COVID-19 patients. In this article, we provide an overview of the pathogenesis of cardiovascular complications secondary to COVID-19 and focus on the mechanisms that may contribute to the likely benefits of n-3 PUFAs and their metabolites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".