Exploratory analyses of leukocyte responses in hospitalized patients treated with ozanimod following a severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection
Bibliographic record
Abstract
ligands inhibit cytokine storm and help preserve lung endothelial barrier integrity. We recently showed that the S1P receptor ligand ozanimod can be safely administered to hospitalized patients with coronavirus disease 2019 (COVID-19) exhibiting severe symptoms of viral pneumonia, with potential clinical benefits. Here, we extend on this study and investigate the impact of ozanimod on key features of the immune response in patients with severe COVID-19. We quantified circulating cytokine levels, peripheral immune cell numbers, proportions and activation status; we also monitored the quality of the humoral response by assessing anti-severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) antibodies. Our findings reveal that patients receiving ozanimod during acute SARS-CoV-2 infection exhibit significantly reduced numbers of circulating monocytes compared with those receiving standard care. Correspondingly, in the ozanimod-treated group, circulating levels of C-C motif ligand 2 (CCL2) were decreased. While treatment with ozanimod negatively impacted the humoral response to COVID-19 in unvaccinated patients, it did not impair the development of a robust anti-SARS-CoV-2 antibody response in vaccinated patients. These findings suggest that ozanimod influences key immune mechanisms during the acute phase of SARS-CoV-2 infection.
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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) | 0.001 | 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".