Nickel allergy is associated with a broad spectrum cytokine response
Bibliographic record
Abstract
BACKGROUND: Nickel-induced proliferation or cytokine release by peripheral blood mononuclear cells may be used for in vitro diagnosis of nickel allergy. OBJECTIVES: Aim of this study was to explore the nickel-specific cytokine profile to further elucidate the pathogenesis of nickel allergic contact dermatitis (ACD) and to identify potential new biomarkers for nickel ACD. METHODS: Peripheral blood mononuclear cells from patients and controls were cultured with T-cell skewing cytokine cocktails and/or nickel. Cytokine and chemokine concentrations were assessed in culture supernatants using validated multiplex assays. Specific cytokine production was related to history of nickel allergy and patch-test results. RESULTS: Twenty-one of the 33 analytes included in the analysis were associated with nickel allergy and included type1 (TNF-α, IFN-γ, TNF-β), type 2 (IL-3, IL-4, IL-5, IL-13), type 1/2 (IL-2, IL-10), type 9 (IL-9), type 17/1 (IL-17A[F], GM-CSF, IL-21) and type 22 (IL-22) derived cytokines as well as the T-cell/antigen presentation cell derived factors Thymus and activation regulated chemokine (TARC), IL-27 and IP-10. Receiver operator characteristics (ROC) analysis showed that IL-5 was the strongest biomarker for nickel allergy. CONCLUSIONS: A broad spectrum of 33 cytokines and chemokines is involved in the allergen-specific immune response in nickel allergic patients. IL-5 remains, next to the lymphocyte proliferation test, the strongest biomarker for nickel allergy.
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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 | 0.001 |
| 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".