Physiologically Based Modeling of the Accumulation in Plasma and Tissue Lipids of a Mixture of PCB Congeners in Female Sprague-Dawley Rats
Bibliographic record
Abstract
This study aimed to develop a physiologically based model for simulating the concentrations of polychlorinated biphenyls (PCBs) in tissue and plasma lipids of rats exposed to PCB mixtures. The model was based on the assumption that the neutral lipid fraction is the only critical determinant of the tissue distribution of PCBs, and that the solubility/retention in other tissue components is negligible. The volumes of the model compartments reflected the volumes of neutral lipids, whereas the flow rates corresponded to those of the neutral lipids in blood. Since the equilibrium ratio of PCB concentrations in neutral lipids of tissues and plasma equals 1, the present modeling approach does not require the use of tissue:blood partition coefficients. Metabolism rates were derived from the best visual fit of the model to the PCB concentrations in hepatic lipids determined on d 41 and 90 in rats exposed to a mixture containing 5, 50, or 500 microg PCBs (118, 138, 153, 170, 180 and 187) per kilogram body weight according to various protocols: (a) every-day dosing, (b) once-a-week dosing, (c) consecutive dosing for 13 d with no further treatment, and (d) 13 irregularly spaced doses. The resulting model consistently simulated the concentrations of PCBs in adipose tissue and plasma lipids of rats exposed according to the four described protocols. The physiologically based pharmacokinetic (PBPK) model developed in this study should be useful as a basis for interpretating blood or plasma lipid concentration data on PCBs collected during biomonitoring studies.
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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 | 0.000 | 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 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".