Toxicokinetics in rats and modeling to support the interpretation of biomonitoring data for rare-earth elements
Bibliographic record
Abstract
Toxicokinetic models are useful tools to better understand the fate of contaminants in the human body and to establish biological guidance values to interpret biomonitoring data in human populations. This research aimed to develop a biologically-based toxicokinetic model for four rare earth elements (REEs), cerium (Ce), praseodymium (Pr), neodymium (Nd) and yttrium (Y), and to establish biomonitoring equivalents (BE) serving as biological guidance values. The model was constructed using physiological data taken from the literature as well as new experimental kinetic data. These new data indicated that REEs readily disappeared from blood and accumulated mostly in the liver; excretion occurred mainly through feces although a small fraction was eliminated in urine. To properly reproduce the observed kinetics, the model was represented as 19 compartments, which include main tissues and their components (such as retention by macrophages) supplied by blood, as well as routes of excretion. The transfer coefficients between compartments were determined numerically by adjustments to experimental data. Simulations gave good fits to available experimental kinetic data and confirmed that the same model structure is applicable to the four elements. BEs of 0.3 µg/L of Pr and Nd were derived from the provisional RfD of 0.5 mg/kg bw/day established by the U.S. EPA. These BEs can be updated according to new reference dose values (RfD). Overall, the model can contribute to a better understanding of the significance of biological measurements and to the inference of exposure levels; it can also be used for the modeling of other REEs. The BEs will further allow rapid screening of different populations using biological measurements in order to guide risk assessments.
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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) | 0.001 | 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 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".