Characterizing Atopic Dermatitis and Abrocitinib Early Response Through Tape-Stripped and Serum Biomarkers
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) is a chronic, multifactorial skin condition with distinct subtypes, each exhibiting unique immune characteristics and treatment responses. Objective: Our study aimed to characterize biomarker profiles of AD subtypes and identify biomarkers that may predict the response to abrocitinib treatment. Methods: Serum and tape strips from lesional and nonlesional skin were collected from patients with AD (n = 50) pre- and post-4 weeks of abrocitinib treatment (n = 15) and 13 healthy controls. We compared serum and skin biomarker expression in 16 patients with extrinsic and 16 with intrinsic AD with matched disease severity. Results: Th1, Th2, Th17/22, general inflammation-related factors, and JAK-STAT signaling molecules were upregulated in the skin and serum of patients with AD. Compared with intrinsic AD, extrinsic AD showed higher FCER1A and chemokine (C-C motif) ligand 22 expression in the skin ( P < 0.05), while intrinsic AD had higher Th17/Th22-related biomarker expression. Multivariate correlation models significantly improved the correlation between the biomarkers and the severity of AD, both before and after treatment. Conclusion: Extrinsic AD shares Th2-type inflammation with intrinsic AD, but intrinsic AD shows elevated Th17/Th22-type inflammation. Biomarker integration models can contribute to precise AD treatment.
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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 | 0.001 | 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".