Exploring ozanimod’s role in reducing lung inflammation in a bacterial ARDS model 2582
Bibliographic record
Abstract
Abstract Description Acute respiratory distress syndrome (ARDS) is a severe complication of infections like bacterial and viral pneumonia, characterized by increased vascular permeability and a heightened cytokine response. While prophylactic use of sphingosine-1-phosphate receptor 1 (S1P1) ligands has shown protective effects in mice against lethal influenza virus exposure by limiting endothelial overactivation, permeability, and cytokine amplification, current knowledge remains largely limited to prophylactic models of ARDS, primarily related to viral infections. Given that bacterial ARDS is prevalent and has a high mortality rate (≈35%), identifying new therapeutic interventions is critical. Here, we used a Streptococcus pneumoniae-induced ARDS model in mice to evaluate whether the S1P1 ligand ozanimod could serve as a viable therapeutic option in bacterial ARDS. Mice were inoculated with S. pneumoniae and treated 24 hours later with antibiotics, with or without ozanimod. This model mimics the progression to ARDS that continues to worsen despite effective antibiotic therapy. Ozanimod treatment accelerated recovery, evidenced by faster weight gain post-infection, and tended to reduce both lung edema (wet lung weight) and neutrophil counts in bronchoalveolar lavage fluid. Ozanimod also significantly reduced CD69 levels on lung lymphocytes. These results suggest that S1P1 targeting may offer therapeutic benefits in bacterial ARDS. Funding Sources Fondation Institut Universitaire de Cardiologie et de Pneumologie de Québec Topic Categories Microbial, Parasitic, and Fungal Immunology (MPF)
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".