Rationale for Evaluating PDE4 Inhibition for Mitigating against Severe Inflammation in COVID-19 Pneumonia and Beyond.
Bibliographic record
Abstract
BACKGROUND: In the absence of definitive anti-viral therapy, there is considerable interest in mitigating against severe inflammatory reactions in coronavirus disease-2019 (COVID-19) pneumonia to improve survival. These reactions are sometimes termed cytokine storm. PDE4 inhibitors (PDE4i) have anti-inflammatory properties with approved indications in inflammatory skin and joint diseases as well as chronic obstructive pulmonary disease (COPD). Furthermore, multiple animal models demonstrate strong anti-inflammatory effects of PDE4i in respiratory models of viral and bacterial infection and also after chemically mediated lung injury. The rationale for PDE4i use in COVID-19 patients comes from the multimodal mechanism of action with cytokine, chemokine, and other key pathway inhibition all achieved with an excellent safety profile. We highlight how PDE4i could be an overlooked treatment from the rheumatologic and respiratory armamentarium, which has potential beneficial immune-modulation for treating severe COVID-19 pneumonia associated with cytokine storms. The proposed use of PDE4i is also supported by age-related immune changes in inflammation severity in PDE4i modifiable pathways in primate coronavirus disease. In conclusion, over-exuberant anti-viral immune responses in older patients with COVID-19 may pose a substantial risk to patient survival and mitigation against such hyper-inflammation with PDE4i, especially with anti-viral agents, is a strategy that need to be pursed, especially in older patients.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".