Toward a Mechanism-Driven Integrated Framework to Link Human Exposure to Multiple Toxic Metal(loid) Species with Environmental Diseases
Bibliographic record
Abstract
The ongoing anthropogenic pollution of the biosphere with As, Cd, Hg and Pb will inevitably result in an increased influx of their corresponding toxic metal(loid) species into the bloodstream of human populations, including children and pregnant women. To delineate whether the measurable concentrations of these inorganic pollutants in the bloodstream are tolerable or implicated in the onset of environmental diseases urgently requires new insight into their dynamic bioinorganic chemistry in the bloodstream–organ system. Owing to the human exposure to multiple toxic metal(loid) species, the mechanism of chronic toxicity of each of these needs to be integrated into a framework to better define the underlying exposure–disease relationship. Accordingly, this review highlights some recent advances into the bioinorganic chemistry of the Cd2+, Hg2+ and CH3Hg+ in blood plasma, red blood cells and target organs and provides a first glimpse of their emerging mechanisms of chronic toxicity. Although many important knowledge gaps remain, it is essential to design experiments with the intent of refining these mechanisms to eventually establish a framework that may allow us to causally link the cumulative exposure of human populations to multiple toxic metal(loid) species with environmental diseases of unknown etiology that do not appear to have a genetic origin. Thus, researchers from a variety of scientific disciplines need to contribute to this interdisciplinary effort to rationally address this public health threat which may require the implementation of stronger regulatory requirements to improve planetary and human health, which are fundamentally intertwined.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".