The Flavonoid Isoliquiritigenin Reduces Lung Inflammation and Mouse Morbidity during Influenza Virus Infection
Bibliographic record
Abstract
The host response to influenza virus infection is characterized by an acute lung inflammatory response in which intense inflammatory cell recruitment, hypercytokinemia, and a high level of oxidative stress are present. The sum of these events contributes to the virus-induced lung damage that leads to high a level of morbidity and mortality in susceptible infected patients. In this context, we identified compounds that can simultaneously reduce the excessive inflammatory response and the viral replication as a strategy to treat influenza virus infection. We investigated the anti-inflammatory and antiviral potential activities of isoliquiritigenin (ILG). Interestingly, we demonstrated that ILG is a potent inhibitor of influenza virus replication in human bronchial epithelial cells (50% effective concentration [EC50] = 24.7 μM). In addition, our results showed that this molecule inhibits the expression of inflammatory cytokines induced after the infection of cells with influenza virus. We demonstrated that the anti-inflammatory activity of ILG in the context of influenza virus infection is dependent on the activation of the peroxisome proliferator-activated receptor gamma pathway. Interestingly, ILG phosphate (ILG-p)-treated mice displayed decreased lung inflammation as depicted by reduced cytokine gene expression and inflammatory cell recruitment. We also demonstrated that influenza virus-specific CD8(+) effector T cell recruitment was reduced up to 60% in the lungs of mice treated with ILG-p (10 mg/kg) compared to that in saline-treated mice. Finally, we showed that administration of ILG-p reduced lung viral titers and morbidity of mice infected with the PR8/H1N1 virus.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".