Molecular signatures order the potency of topically applied anti-inflammatory drugs in patients with atopic dermatitis
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) presents a large unmet need for treatments with better safety and efficacy. To facilitate development of topical therapeutics, we need an efficient model for assessing different formulations and concentrations. The "plaque model" has been successfully implemented in patients with psoriasis, another common inflammatory disease, to assess the efficacy of topical treatments. This model has not been validated for AD, which has higher placebo responses and less stable lesions than psoriasis. OBJECTIVE: We aimed to assess changes in molecular signatures of intrapatient target lesions treated with topical therapeutics. METHODS: We enrolled 30 patients with mild-to-moderate AD in a randomized, double-blind, intraindividual comparison of 3 approved agents applied blindly at the investigator site daily for 14 days: pimecrolimus, betamethasone dipropionate, clobetasol propionate, and a vehicle/emollient control. Changes in total sign scores (TSSs), transepidermal water loss, and tissue biomarkers (determined by using RT-PCR and immunohistochemistry) were evaluated. RESULTS: TSSs showed improvements of 30%, 40%, 68%, and 76% at 2 weeks with vehicle, pimecrolimus, betamethasone, and clobetasol, respectively, with parallel changes in transepidermal water loss (P < .05). Significant differences versus vehicle values were limited to steroids (P < .0001). Steroids (particularly clobetasol) restored epidermal hyperplasia and terminal differentiation versus minimal changes with vehicle or pimecrolimus (P < .001). Levels of cellular infiltrates and cytokines (IL-13, IL-22, and S100As) were similarly reduced only by steroids (P < .001). TSS improvement correlated with changes in hyperplasia, infiltrates, and differentiation markers. CONCLUSION: We detected significant clinical and tissue differences between agents, providing a novel approach to study the differential effects of topical formulations using a limited sample size.
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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) | 0.001 | 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".