Association between enteral essential fatty acids and plasma phospholipid essential fatty acids related immune response in critically ill adults with COVID‐19: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a complicated disease with widely varying outcomes. Up to 20% of unvaccinated, hospitalized patients infected with COVID-19 may die during the initial three weeks. Our research shows that COVID-19 infection results in rapid, remarkable change in the balance between essential fatty acid constituents of plasma phospholipids that are substrates for synthesis of signals that regulate immunity, inflammation, and thrombosis. METHODS: We assessed if enteral feeding of EPA (eicosapentaenoic acid, 20:5n-3) and DHA (docosahexaenoic acid; 22:6n-3) normalizes remodeling of plasma phospholipid essential fatty acid content caused by COVID-19 viral infection and modifies immune response. Blood samples were taken on day 1 of hospital admission. From the patient record, patients were categorized into two groups based on enteral formula fed by day 5 after admission: enteral feeds that contained EPA + DHA or not. These two groups were compared at 1 week and 3 weeks postadmission for plasma phospholipid fatty acids, cytokines, and chemokines. RESULTS: Feeding EPA + DHA increases plasma content of these fatty acids in specific species of plasma phosphatidylcholine. Change in essential fatty acid status was associated with downregulation of the inflammatory signal macrophage inflammatory protein-1β and increase in interleukin-17, monocyte chemoattractant protein (MCP)-4, macrophage-derived chemokine and thymus- and activation-regulated chemokine signals. Plasma arachidonic acid content correlated with chemoattractant protein MCP-4 during early stages of infection. CONCLUSION: We conclude that feeding COVID-19 infected intensive care unit patients enteral formulas containing EPA and DHA may alter response to infection; however, the potential benefit to clinical outcome is not clear.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".