Textile Dyes Disperse Orange 1 and Yellow 3 Contain More Than One Allergen As Shown by Patch Testing with Thin-Layer Chromatograms
Bibliographic record
Abstract
BACKGROUND: It is known that some patch-test preparations containing disperse dyes contain impurities with unknown relevance for the development or elicitation of contact allergy. OBJECTIVE: To evaluate the significance of the impurities found in the commercial dyes Disperse Orange 1 (DO1) and Disperse Yellow 3 (DY3) regarding contact allergy in patients with known sensitivity to them. METHODS: Ten patients allergic to DY3 and/or DO1 were tested with a dilution series of commercial and purified DY3 and DO1 (with water-soluble parts prepared from the commercial dyes) and with naphthalene sulfonate. Nine patients were additionally tested with thin-layer chromatograms (TLCs) made from the commercial DO1 and DY3 and with paper chromatograms made from the water-soluble part of these dyes. RESULTS: Eight of nine and three of six patients tested positively to the TLCs of DO1 and DY3, respectively. Among them, 4 of 8 and 2 of 3 patients, respectively, were positive also to another spot on the TLCs. One patient was positive to the paper chromatogram from the water-soluble part of DO1. None of the tested patients reacted to naphthalene sulfonate. CONCLUSION: The results of our study suggest that there are more relevant allergens in the fat-soluble and water-soluble fractions of the commercial disperse dyes.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".