Delayed type hypersensitivity reactions to various allergens may differently model inflammatory skin diseases
Bibliographic record
Abstract
BACKGROUND: Treatment of inflammatory skin diseases, including atopic dermatitis (AD) and psoriasis, is undergoing transformative changes, highlighting the need to develop experimental models of skin inflammation in humans to predict treatment responses. METHODS: We topically or intradermally administered four common sensitizers (dust mite (DM), diphencyprone (DPCP), nickel (Ni), and purified protein derivative (PPD)) to the backs of 40 healthy patients and the skin hypersensitivity response was biopsied and evaluated using immunohistochemistry, RNA-seq, and RT-PCR. RESULTS: All agents induced strong increases in cellular infiltrates (T-cells and dendritic cells) as compared to untreated skin (p < .05), with variable T helper polarization. Overall, DPCP induced the strongest immune responses across all pathways, including innate immunity (IL-1α, IL-8), Th1 (IFNγ, CXCL10), Th2 (IL-5, CCL11), and Th17 (CAMP/LL37) products, as well as the highest regulatory tone (FOXP3, IL-34, IL-37) (FDR <0.01). Nickel induced Th17 (IL-17A), Th1 (CXCL10) and Th2 (IL-4R) immune responses to a lesser extent than DPCP (p < .05). PPD induced predominantly Th1 (IFNγ, CXCL10, STAT1) and Th17 inflammation (IL-17A) (p < .05). DM induced modulation of Th2 (IL-13, CCL17, CCL18), Th22 (IL-22), and Th17/Th22 (S100A7/9/12) pathways (p < .05). Barrier defects that characterize both AD and psoriasis were best modeled by DPCP and Ni, followed by PPD, including downregulation of terminal differentiation (FLG, FLG2, LOR, LCEs), tight junction (CLDN1/CLDN8), and lipid metabolism (FA2H, FABP7)-related markers. CONCLUSION: Our data imply that DPCP induced the strongest immune response across all pathways, and barrier defects characteristic of AD and psoriasis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".