Inflammatory pathways in COVID‐19: Mechanism and therapeutic interventions
Bibliographic record
Abstract
The 2019 coronavirus disease (COVID-19) pandemic has become a global crisis. In the immunopathogenesis of COVID-19, SARS-CoV-2 infection induces an excessive inflammatory response in patients, causing an inflammatory cytokine storm in severe cases. Cytokine storm leads to acute respiratory distress syndrome, pulmonary and other multiorgan failure, which is an important cause of COVID-19 progression and even death. Among them, activation of inflammatory pathways is a major factor in generating cytokine storms and causing dysregulated immune responses, which is closely related to the severity of viral infection. Therefore, elucidation of the inflammatory signaling pathway of SARS-CoV-2 is important in providing otential therapeutic targets and treatment strategies against COVID-19. Here, we discuss the major inflammatory pathways in the pathogenesis of COVID-19, including induction, function, and downstream signaling, as well as existing and potential interventions targeting these cytokines or related signaling pathways. We believe that a comprehensive understanding of the regulatory pathways of COVID-19 immune dysregulation and inflammation will help develop better clinical therapy strategies to effectively control inflammatory diseases, such as COVID-19.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".