Using chemical speciation modelling to discuss variations in patch test reactions to different aluminium and chromium salts
Bibliographic record
Abstract
BACKGROUND: Allergic contact dermatitis to metals is diagnosed by applying a metal salt in a patch test. The bioavailability of the metal salt might depend on the choice of metal salt, the concentration, sweat composition, and pH. OBJECTIVES: The main purpose of this study was to apply chemical speciation modelling, which is based on experimentally derived input data and calculates the concentrations of chemical forms (species) in solutions, to reproduce and discuss clinical patch test results of aluminium and chromium. METHODS: Joint Expert Speciation System (JESS), Hydra/Medusa, and Visual MINTEQ were employed to study the bioavailable fraction and chemical form of clinically applied aluminium and chromium salts as a function of salt type, applied concentration, sweat composition, and pH. RESULTS: Investigated aluminium and chromium salts can have a very low bioavailability with a large dependency on sweat composition, pH, metal salt, and concentration. Both aluminium and chromium ions could shift the pH towards acidic or basic values based on their chemical form. CONCLUSIONS: Reported seasonal and interpatient variability in positive reactions to aluminium is likely related to sweat pH and composition. Potassium dichromate increases the pH, whereas aluminium and trivalent chromium chloride strongly decrease the pH, possibly increasing skin diffusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".