High levels of eicosanoids and docosanoids in the lungs of intubated COVID‐19 patients
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 is responsible for coronavirus disease 2019 (COVID‐19). While COVID‐19 is often benign, a subset of patients develops severe multilobar pneumonia that can progress to an acute respiratory distress syndrome. There is no cure for severe COVID‐19 and few treatments significantly improved clinical outcome. Dexamethasone and possibly aspirin, which directly/indirectly target the biosynthesis/effects of numerous lipid mediators are among those options. Our objective was to define if severe COVID‐19 patients were characterized by increased bioactive lipids modulating lung inflammation. A targeted lipidomic analysis of bronchoalveolar lavages (BALs) by tandem mass spectrometry was done on 25 healthy controls and 33 COVID‐19 patients requiring mechanical ventilation. BALs from severe COVID‐19 patients were characterized by increased fatty acids and inflammatory lipid mediators. There was a predominance of thromboxane and prostaglandins. Leukotrienes were also increased, notably LTB 4 , LTE 4 , and eoxin E 4 . Monohydroxylated 15‐lipoxygenase metabolites derived from linoleate, arachidonate, eicosapentaenoate, and docosahexaenoate were also increased. Finally yet importantly, specialized pro‐resolving mediators, notably lipoxin A 4 and the D‐series resolvins, were also increased, underscoring that the lipid mediator storm occurring in severe COVID‐19 involves pro‐ and anti‐inflammatory lipids. Our data unmask the lipid mediator storm occurring in the lungs of patients afflicted with severe COVID‐19. We discuss which clinically available drugs could be helpful at modulating the lipidome we observed in the hope of minimizing the deleterious effects of pro‐inflammatory lipids and enhancing the effects of anti‐inflammatory and/or pro‐resolving lipid mediators.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".