Lymphocyte subpopulations in human exposure to metals (IUPAC Technical Report)
Bibliographic record
Abstract
Abstract Numerous species of metal ions cause immunosensitization in humans. Possible approaches to determine those occupational and environmental exposures to metals that result in immunological changes include lymphocyte transformation assay, cytokine profiling, and measurement of lymphocyte subpopulations. In two previous papers, we considered lymphocyte transformation assay [1] and cytokine profiling [2]. Here we review the effects of exposures to metals on lymphocyte subpopulations. Specific consideration is given to beryllium, chromium, cobalt, nickel, palladium and platinum, cadmium, gold, mercury, and lead. Analysis of the scientific literature shows that immunosensitizing metals may have influences on the lymphocyte subset composition, but only in a few instances does exposure to metals cause reproducible shifts of lymphocyte subpopulations. If lymphocyte subpopulations are analyzed, each diagnostic step, including indication, sample handling, analytic procedure, and data interpretation, should adhere to good quality assurance and quality control.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".