Identification of Phenolic Dermal Sensitizers in a Wound Closure Tape
Bibliographic record
Abstract
A latex-allergic patient presented with a severe local reaction to a non-latex wound closure bandage following surgery. Extracts of the bandage were analyzed by gas chromatograph-electron impact-mass spectrometry (GC EI-MS) in the total ion monitoring mode. Components were identified by their ion mass fingerprint and elution time as a corresponding standard from the GC column. The chemicals identified were 4,4'-thiobis-(6-tert-butyl-m-cresol) (TBBC), 6-tert-Butyl-m-cresol (BC), 2,4-di-tert-butylphenol (BP) and erucamide (EA). Sensitization potential of these chemicals was evaluated using two quantitative structure-activity relationship (QSAR) programs. The phenol 2,6-di-tert-butyl-4-(hydroxymethyl)phenol (BHP) was also included in the test series. It was initially thought to be present in the bandage but detectable levels could not be confirmed. The potential for TBBC to induce a sensitization response was predicted by both Derek for Windows and TOPKAT 6.2. The potential for BC and BP to induce a sensitization response was predicted by Derek for Windows, but not TOPKAT. BHP and EA were not predicted to be sensitizers by either QSAR program. Local lymph node assay (LLNA) analysis of the chemicals identified TBBC, BP, and BC as potential sensitizers with EC3 values between 0.2 and 4.5%. None of the animals exhibited body weight loss or skin irritation at the concentrations tested. In agreement with the toxicological modeling, BHP did not induce a sensitization response in the LLNA. Following a positive LLNA response, TBBC, BP, and BC were further characterized by phenotypic analysis of the draining lymph nodes. A positive LLNA result coupled with a lack of increase in B220(+)IgE(+) cell and serum IgE characterize these chemicals as Type IV sensitizers. These studies used a multidisciplinary approach combining clinical observation, GC-EI-MS for chemical identification, QSAR modeling of chemicals prior to animal testing, and the LLNA for determination of the sensitization potential of chemicals in a manufactured product.
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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 | 0.001 | 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".