Chemokines and eicosanoids fuel the hyperinflammation within the lungs of patients with severe COVID-19
Bibliographic record
Abstract
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to a variety of clinical outcomes, ranging from the absence of symptoms to severe acute respiratory disease and ultimately death. A feature of patients with severe coronavirus disease 2019 (COVID-19) is the abundance of inflammatory cytokines in the blood. Elevated levels of cytokines are predictive of infection severity and clinical outcome. In contrast, studies aimed at defining the driving forces behind the inflammation in lungs of subjects with severe COVID-19 remain scarce. OBJECTIVE: Our aim was to analyze and compare the plasma and bronchoalveolar lavage (BAL) fluids of patients with severe COVID-19 (n = 45) for the presence of cytokines and lipid mediators of inflammation (LMIs). METHODS: Cytokines were measured by using Luminex multiplex assay, and LMIs were measured by using liquid chromatography-tandem mass spectrometry. RESULTS: We revealed high concentrations of numerous cytokines, chemokines, and LMIs in the BAL fluid of patients with severe COVID-19. Of the 13 most abundant mediators in BAL fluid, 11 were chemokines, with CXCL1 and CXCL8 being 200 times more abundant than IL-6 and TNF-α. Eicosanoid levels were also elevated in the lungs of subjects with severe COVID-19. Consistent with the presence chemotactic molecules, BAL fluid samples were enriched for neutrophils, lymphocytes, and eosinophils. Inflammatory cytokines and LMIs in plasma showed limited correlations with those present in BAL fluid, arguing that circulating inflammatory molecules may not be a reliable proxy of the inflammation occurring in the lungs of patients with severe COVID-19. CONCLUSIONS: Our findings indicate that hyperinflammation of the lungs of patients with severe COVID-19 is fueled by excessive production of chemokines and eicosanoids. Therapeutic strategies to dampen inflammation in patients with COVID-19 should be tailored accordingly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".