Ethnic endotypes in paediatric atopic dermatitis depend on immunotype, lipid composition and microbiota of the skin
Bibliographic record
Abstract
Background: Atopic dermatitis (AD) endotypes differ with ethnicity. We examined the skin microbiota, cytokine and lipid profiles in Greenlandic Inuit and Danish children with AD.MethodsTwenty‐five Inuit children with AD and 25 Inuit control children were clinically examined and compared to previously collected data from 25 Danish children with AD. Skin tape strips and skin swabs were collected from lesional and non‐lesional skin. Levels of cutaneous immune biomarkers, free sphingoid bases and their (glycosyl)ceramides were analysed. Skin swabs were analysed with 16S rRNA and tuf gene for characterization of bacterial species communities.ResultsBacterial β‐diversity was significantly different between Inuit and Danish AD skin, in both lesional (p < 0.001) and non‐lesional (p < 0.001) AD skin, and there was a higher relative abundance of Staphylococcus aureus in Danish compared to Inuit lesional (53% vs. 8%, p < 0.01) and non‐lesional skin (55% vs. 5%, p < 0.001). Danish AD children had a higher α‐diversity than Inuit children in non‐lesional (p < 0.05) but not in lesional skin. Significantly higher levels of type 2 immunity cytokine interleukin (IL)‐4 (p < 0.05) and IL‐5 (p < 0.01) were identified in Inuit compared to Danish AD children. In contrast, IL‐33 (p < 0.01) was higher in Danish lesional and non‐lesional AD skin. Higher levels of long‐chain glucosylceramide (GlcCER)[S](d26:1) were found in lesional (p < 0.001) and non‐lesional (p < 0.001) Inuit skin compared with Danish AD skin. NMF levels were similar in Inuit and Danish AD skin.ConclusionSkin microbiota, cytokine and lipid composition differed significantly between Inuit and Danish children with AD and showed a stronger type 2 immune signature in Inuit children.
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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) | 0.002 | 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".