Prominent Role of Type 2 Immunity in Skin Diseases: Beyond Atopic Dermatitis
Bibliographic record
Abstract
Type 2 immunity, illustrated by T helper 2 lymphocytes (Th2) and downstream cytokines (IL-4, IL-13, IL-31) as well as group 2 innate lymphoid cells (ILC2), is important in host defense and wound healing. 1 The hallmark of type 2 inflammation is eosinophilia and/or high IgE counts and is best recognized in atopic diathesis. Persistent eosinophilia, such as seen in hypereosinophilic syndromes, leads to fibrosis and hence therapeutic Type 2 inhibition in fibrotic diseases is of high interest. Furthermore, as demonstrated in cutaneous T cell lymphoma, advanced disease is characterized by Th1 to Th2 switch allowing cancer progression and immunosuppression. Development of targeted monoclonal antibodies against IL-4Rα (eg, dupilumab) led to a paradigm shift for the treatment of atopic dermatitis (AD) and stimulated research to better understand the role of Type 2 inflammation in other skin conditions. In this review, we summarize up to date knowledge on the role of Type 2 inflammation in skin diseases other than AD and highlight whether the use of Type 2 targeted therapies has been documented or is being investigated in clinical trials. This manuscript reviews the role of Type 2 inflammation in dermatitis, neurodermatitis, IgE-mediated dermatoses (eg, bullous pemphigoid, chronic spontaneous urticaria), sclerodermoid conditions and skin neoplasms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".