IL-15 and IL-17F are differentially regulated and expressed in mycosis fungoides (MF)
Bibliographic record
Abstract
Skin lesions from mycosis fungoides (MF) patients display an increased expression of interleukin-15 (IL-15), IL-17F, and other cytokines implicated in inflammation and malignant cell proliferation in cutaneous T-cell lymphoma (CTCL). In the leukemic variant of CTCL, Sézary syndrome (SS), IL-2 and IL-15 trigger activation of the Jak-3/STAT3 pathway and transcription of IL17A gene, whereas it is unknown what causes IL-15 expression, Jak3/STAT3 activation, and production of IL-17F in MF. Here, we studied the expression and regulation of IL-15 and its relation to IL-17F in MF cell lines and skin lesions from 60 MF patients. We show that: (1) the spontaneous IL-15 mRNA expression is resistant to Jak3 and STAT3 inhibitors at concentrations that profoundly inhibit STAT3 activation and IL-17F mRNA expression; (2) anti-IL-15 antibody blocks STAT3 activation induced by exogenous IL-15 in non-malignant MF T cells, whereas the spontaneous STAT3 activation and IL-17F expression in malignant T cells is not inhibited; (3) patients display heterogeneous IL-15/IL-17F mRNA expression patterns in skin lesions; and (4) IL-15 expression (in contrast to IL-17F) is not associated with progressive disease. Taken together, these findings indicate that IL-15 and IL-17F are differentially regulated and expressed in MF. We propose that IL-15 and IL-17F are markers for different inflammatory environments and play distinct roles in the development and progression of MF.
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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".