Immunological Effects of Mercury
Bibliographic record
Abstract
Various chemical species of mercury differ considerably with regard to their route of absorption and their distribution in the body, yet many of them and their metabolites exhibit high-affinity binding to sulfanyl groups of proteins. Among all metals, mercury appears to have the most diverse effects on the immune system. Depending on the animal species and experimental conditions, mercury compounds may cause immunosuppression or immunostimulation, autoimmune reactions, or hypersensitivity. Mercury-sensitive strains of rats and mice are often used as model organisms to study the time course and events in autoimmunity. Within about 14 days after onset of oral mercury(II) exposure, levels of immunoglobulins E and G (IgE and IgG) increase, including autoantibodies to biomolecules such as laminin and fibrillarin. Antigen-antibody complexes are formed and are the cause of subsequent autoimmune diseases of blood vessels and organs. Mercury may induce local mercury hypersensitivity in humans, but the evidence for a role of mercury in autoimmune disease of humans is at best weak. Models for the immune effects of mercury are presented on the basis of current knowledge.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".