Investigating potential exogenous tumor initiating and promoting factors for Cutaneous T-Cell Lymphomas (CTCL), a rare skin malignancy
Bibliographic record
Abstract
Most skin malignancies are caused by external and often preventable environmental agents. Multiple reports demonstrated that cutaneous T-cell lymphomas (CTCL) can occur in married couples and cluster in families. Furthermore, recent studies document geographic clustering of this malignancy in Texas as well as in other areas of the United States. Multiple infectious, occupational, and medication causes have been proposed as triggers or promoters of this malignancy including hydrochlorothiazide diuretics, Staphylococcus aureus, dermatophytes, Mycobacterium leprae, Chlamydia pneumoniae, human T-Cell lymphotropic virus type 1 (HTLV1), Epstein-Barr virus (EBV), and herpes simplex virus (HSV). In this report, we review recent evidence evaluating the involvement of these agents in cancer initiation/progression. Most importantly, recent molecular experimental evidence documented for the first time that S. aureus can activate oncogenic STAT3 signaling in malignant T cells. Specifically, S. aureus Enterotoxin type A (SEA) was recently shown to trigger non-malignant infiltrating T cells to release IL-2 and other cytokines. These signals upon binging to their cognate receptors on malignant T cells are then able to activate STAT3 and STAT5 oncogenic signaling and promote cancer progression and IL-17 secretion. In light of these findings, it might be important for patients with exacerbation of their CTCL symptoms to maintain high index of suspicion and treat these individuals for S. aureus colonization and/or sepsis with topical and systemic antibiotics.
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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 | 0.001 | 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".