The Role of Interleukin 13 in Classical Hodgkin Lymphoma
Bibliographic record
Abstract
The Reed-Sternberg (RS) cells of classical Hodgkin lymphoma (cHL) produce several cytokines, which are thought to account for the unique clinical and pathologic features of this disease. We previously identified interleukin (IL)-13 expression as a common feature of cHL and have studied the potential role of this cytokine as an autocrine growth factor for RS cells. IL-13 and the IL-13-specific receptor chain (IL-13R alpha1) are frequently expressed in cHL-derived cell lines and in RS cells from biopsies of cHL tissues. In contrast, IL-13 expression in non-Hodgkin lymphoma (NHL) is uncommon. Neutralization of IL-13 in cultures of cHL-derived cell lines HDLM-2 and L-1236 leads to a dose-dependent inhibition of proliferation, and is associated with increased apoptosis in L-1236 cells. IL-13 neutralization also decreased activation of signal transducer and activator of transcription (STAT)6, an important mediator of IL-13 function. Moreover, STAT6 is often activated in RS cells from primary tumor samples, implying that IL-13 signaling is occurring in these cells in vivo. This review will describe the biologic activities of IL-13 in the immune system, and summarize the evidence implicating IL-13 as an autocrine growth factor for RS cells in cHL. Finally, we will discuss the potential influence of IL-13 on the reactive inflammatory infiltrate that is characteristic of cHL.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".