Mechanisms of immunosensitization to metals (IUPAC Technical Report)
Bibliographic record
Abstract
Abstract A project is underway within IUPAC to evaluate and harmonize the use of various biomarkers for immunosensitization to metals. This present review summarizes our knowledge of the mechanisms by which certain trace elements evoke allergenicity. Some physiological electrolytes (e.g., Na+, K+) and macronutrients (e.g., Ca2+, Fe3+) are immunologically inactive. However, some trace elements essential for cell function (e.g., Co2+, Cu2+, Cr3+), as well as nonessential elements generally considered toxic (e.g., Hg species) or in use as therapeutic agents (e.g., some species of Pt and Au), can give rise to adverse immune reactions. Specific immunological responses to Ni, Co, Cr, Hg, Be, Cu, Pt, Pd, Ir, In, and Au are discussed. In general, these elements can activate T or B cells by specific receptor interactions, resulting in clonal expansion of a metal-specific lymphocyte and an immune response (typically dermatitis) upon re-exposure. Compelling evidence points to the primary role of the T cell in responding to the metal. T-cell activation occurs when a protein of the major histocompatibility complex (MHC) binds to a T-cell receptor in the presence of an MHC binding peptide. Many antigenic substances result in presentation of MHC bound antigenic peptides to the T-cell receptor; metal ions appear to act as haptens that directly or indirectly cause structural changes in MHC molecule-peptide complexes that result in recognition of these complexes by specific T-cell subsets. Nickel and gold are particularly instructive in understanding mechanisms, and are used to discuss models in which the metal may bind to the antigenic peptide (i) before or (ii) after its association with the MHC/T-cell receptor complex, (iii) may bind to the MHC/receptor complex prior to recruitment of the antigenic peptide, or (iv) may bind to the formed peptide/MHC/receptor complex through ligands contributed by one or more of the components.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".