Dupilumab significantly improves skin barrier function in patients with moderate‐to‐severe atopic dermatitis
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is characterized by abnormal skin lipids that are largely driven by hyperactivated type 2 immune responses. The antibody to the α-subunit of interleukin (IL)-4 receptor, dupilumab, was recently approved to treat AD and demonstrated strong efficacy. However, the role of dupilumab therapy in the regulation of skin barrier structure and function has not been fully explored. METHODS: We have evaluated the content of lipids and transepidermal water loss (TEWL) in lesional and non-lesional skin of adults and adolescents with moderate-to-severe AD over the course of 16-week treatment with dupilumab and compared those values with that of matched healthy volunteers. RESULTS: Dupilumab treatment provided a significant decrease in TEWL in AD lesions, lowering it almost to the levels seen in the skin of healthy subjects. Blocking IL-4/IL-13 signaling with dupilumab normalized lipid composition (decreased levels of ceramides with non-hydroxy fatty acids and C18-sphingosine and increased the level of esterified omega-hydroxy fatty acid-containing ceramides) and increased ceramide chain length in lesional as well as non-lesional stratum corneum of AD patients. Partial changes for these parameters were already observed after 2 weeks, with a full response achieved after 8 weeks of dupilumab treatment. CONCLUSIONS: Inhibition of IL-4/IL-13 signaling by dupilumab allows restoration of skin lipid composition and barrier function in patients with moderate-to-severe AD.
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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 | 0.000 |
| 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) | 0.001 | 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".