Skin Barrier Biomarkers in Patch‐Induced and Clinical Allergic and Irritant Contact Dermatitis
Bibliographic record
Abstract
BACKGROUND: Skin barrier impairment is central to irritant (ICD) and allergic contact dermatitis (ACD). Stratum corneum (SC) components cholesterol sulphate (CholSulph), glucosylcholesterol (CholGlc) and natural moisturising factor (NMF) are critical for barrier function, but their changes in ICD and ACD remain underexplored. OBJECTIVES: To measure CholSulph, CholGlc, NMF and IL-1α in patch-induced ICD and ACD and in hand dermatitis (HD) diagnosed as ICD or ACD. METHODS: SC samples were collected from HD patients undergoing patch testing. Biomarkers were analysed in positive reactions to sodium lauryl sulphate (ICD, n = 44), allergens (ACD, n = 113; nickel, chromium, methylisothiazolinone [MI]), lesional HD skin (n = 45) and control (empty chamber, n = 121). RESULTS: CholGlc was significantly elevated in patch-induced ICD and ACD. CholSulph was increased in ICD and chromium- and MI-induced ACD. NMF decreased in ICD, while IL-1α decreased in ICD and chromium ACD. Chromium induced the strongest response, nickel the weakest. In HD, ICD and ACD showed elevated CholGlc, reduced NMF and IL-1α, with CholSulph increased only in ACD. No biomarker differences were detected between clinical ICD and ACD. CONCLUSIONS: Both induced and clinical ICD and ACD show consistent SC biomarker changes reflecting barrier dysfunction, with no differences between clinical ICD and ACD.
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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 | 0.001 |
| 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".