Toxic Metal Species and ‘Endogenous’ Metalloproteins at the Blood–Organ Interface: Analytical and Bioinorganic Aspects
Bibliographic record
Abstract
Globally, human exposure to environmental pollutants causes an estimated 9 million deaths per year and it could also be implicated in the etiology of diseases that do not appear to have a genetic origin. Accordingly, there is a need to gain information about the biomolecular mechanisms that causally link exposure to inorganic environmental pollutants with distinct adverse health effects. Although the analysis of blood plasma and red blood cell (RBC) cytosol can provide important biochemical information about these mechanisms, the inherent complexity of these biological matrices can make this a difficult task. In this perspective, we will examine the use of metalloentities that are present in plasma and RBC cytosol as potential exposure biomarkers to assess human exposure to inorganic pollutants. Our primary objective is to explore the principal bioinorganic processes that contribute to increased or decreased metalloprotein concentrations in plasma and/or RBC cytosol. Furthermore, we will also identify metabolites which can form in the bloodstream and contain essential as well as toxic metals for use as exposure biomarkers. While the latter metal species represent useful biomarkers for short-term exposure, endogenous plasma metalloproteins represent indicators to assess the long-term exposure of an individual to inorganic pollutants. Based on these considerations, the quantification of metalloentities in blood plasma and/or RBC cytosol is identified as a feasible research avenue to better understand the adverse health effects that are associated with chronic exposure of various human populations to inorganic pollutants. Exposure to these pollutants will likely increase as a consequence of technological advances, including the fast-growing applications of metal-based engineering nanomaterials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".