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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".