Elevated functional Interleukin-15 provided by human peripheral immune cells in Multiple Sclerosis patients (150.8)
Bibliographic record
Abstract
Abstract Activated CD8 T cells have been involved in several autoimmune diseases, including Multiple Sclerosis (MS); CD8 T cells from MS patients bear an enhanced activation profile compared to those from controls; but the role of cytokines in boosting CD8 T cell activation in MS is not elucidated. While elevated levels of IL-15, a key cytokine for memory CD8 T cells, has been reported in serum and on peripheral leukocytes in MS patients compared to controls, no study has addressed whether these increased levels influence CD8 T cell functions. The goal of our study is to assess surface IL-15 and IL-15Rα expression on human leukocytes and determine whether these expression levels are sufficient to enhance human CD8 T cell functions. An increased proportion of ex vivo B cells from MS patients express IL-15 compared to controls, and upon contact with CD40L, B cells obtained from controls increased IL-15 expression, reaching levels similar to those observed in MS patients. We use in vitro flow cytometry-based assays to show that effector functions (granzyme B) in CD8 T cells are significantly enhanced upon co-culture with IL-15-expressing B cells. IL-15 has a greater impact on CD8 T cells from MS patients than controls and we provide evidence that CD8 T cells pre-exposed to IL-15 are more cytotoxic than untreated CD8 T cells. We not only demonstrate that multiple leukocytes can modulate CD8 T cell responses by providing IL-15, but also support a role for B cells in MS pathogenesis.
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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) | 0.002 | 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".