Rapid reduction in Staphylococcus aureus in atopic dermatitis subjects following dupilumab treatment
Bibliographic record
Abstract
Background Atopic dermatitis (AD) is an inflammatory disorder characterized by dominant type 2 inflammation leading to chronic pruritic skin lesions, allergic comorbidities and Staphylococcus aureus skin colonization and infections. S. aureus is thought to play a role in AD severity. Objective We characterized the changes in the host-microbial interface in AD subjects following type 2 blockade with dupilumab. Methods Participants (n=71) with moderate-severe AD were enrolled in a randomized (dupilumab vs placebo; 2:1), double-blind study at Atopic Dermatitis Research Network centers. Bioassays were performed at multiple timepoints: S. aureus and virulence factor quantification, 16s rRNA microbiome, serum biomarkers, skin transcriptomic analyses and peripheral blood T-cell phenotyping. Results At baseline, 100% of participants were S. aureus colonized on the skin surface . Dupilumab treatment resulted in significant reductions in S. aureus after only 3 days (compared to placebo); 11 days before clinical improvement. Participants with the greatest S. aureus reductions had the best clinical outcomes, and these reductions correlated with reductions in serum CCL17 and disease severity. Reductions (10-fold) in S. aureus cytotoxins (day 7), perturbations in Th17 subsets (day 14), and increased expression of genes relevant for IL-17, neutrophil and complement pathways (day 7) were also observed. Conclusion Blockade of IL-4 and IL-13 signaling, very rapidly (day 3) reduces S. aureus abundance in AD subjects, and this reduction correlates with reductions in the type 2 biomarker, CCL17 and measures of AD severity (excluding itch). Immunoprofiling and/or transcriptomics suggest a role for Th17, neutrophils and complement activation as potential mechanisms to explain these findings.
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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 | 0.000 | 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 | 0.001 |
| 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".