Weathering the Storm: Harnessing the Resolution of Inflammation to Limit COVID-19 Pathogenesis
Bibliographic record
Abstract
The resolution of inflammation is a temporally and spatially coordinated process that in its innate manifestations, primarily involves neutrophils and macrophages. The shutdown of infection or injury-induced acute inflammation requires termination of neutrophil accumulation within the affected sites, neutrophil demise, and clearance by phagocytes (efferocytosis), such as tissue-resident and monocyte-derived macrophages. This must be followed by macrophage reprogramming from the inflammatory to reparative and consequently resolution-promoting phenotypes and the production of resolution-promoting lipid and protein mediators that limit responses in various cell types and promote tissue repair and return to homeostatic architecture and function. Recent studies suggest that these events, and macrophage reprogramming to pro-resolving phenotypes in particular, are not only important in the acute setting, but might be paramount in limiting chronic inflammation, autoimmunity, and various uncontrolled cytokine-driven pathologies. The SARS-CoV-2 (COVID-19) pandemic has caused a worldwide health and economic crisis. Severe COVID-19 cases that lead to high morbidity are tightly associated with an exuberant cytokine storm that seems to trigger shock-like pathologies, leading to vascular and multiorgan failures. In other cases, the cytokine storm can lead to diffuse alveolar damage that results in acute respiratory distress syndrome (ARDS) and lung failure. Here, we address recent advances on effectors in the resolution of inflammation and discuss how pro-resolution mechanisms with particular emphasis on macrophage reprogramming, might be harnessed to limit the universal COVID-19 health threat.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".