Ex Vivo Overactivation of Lymphocyte Subsets in Fibrotic Hypersensitivity Pneumonitis Is Blunted by a Sphingosine-1-Phosphate Receptor Ligand
Bibliographic record
Abstract
Lymphocytes are central to the pathogenesis of hypersensitivity pneumonitis and a strong body of evidence supports that lymphocytes are modulated by sphingosine-1-phosphate receptor-modifying drugs. This exploratory study aimed to determine if a pharmacological sphingosine-1-phosphate receptor ligand interfered with the activation of lymphocytes obtained from fibrotic hypersensitivity pneumonitis patients. Peripheral blood mononuclear cells of 12 patients and 10 control subjects were submitted to CD3/CD28 stimulation, isolated B cells were incubated with a TLR9 ligand; and we tested how these stimulations were impacted by ozanimod, a sphingosine-1-phosphate receptor ligand. T cell and B cell subsets from patients overexpressed CD69 and cytokines such as TNF and IL-4 in response to CD3/CD28 stimulation, compared to controls. In patients with fibrotic hypersensitivity pneumonitis, ozanimod alleviated CD3/CD28 induction of CD69, IL-4, and TNF in CD8, but not CD4 T cells. In isolated B cells stimulated with a TLR9 ligand, ozanimod reduced cell surface expression of CD69, CD86, and CD40, as well as TNF and IL-6 accumulation in supernatant. We conclude that lymphocyte subsets are functionally impacted in patients with fibrotic hypersensitivity pneumonitis and that ozanimod can interfere ex vivo with the overactivation of B cells and CD8 T cells in response to specific stimuli.
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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".