Editor’s Highlight: Congener-Specific Disposition of Chiral Polychlorinated Biphenyls in Lactating Mice and Their Offspring: Implications for PCB Developmental Neurotoxicity
Bibliographic record
Abstract
Chiral polychlorinated biphenyl (PCB) congeners have been implicated by laboratory and epidemiological studies in PCB developmental neurotoxicity. These congeners are metabolized by cytochrome P450 (P450) enzymes to potentially neurotoxic hydroxylated metabolites (OH-PCBs). The present study explores the enantioselective disposition and toxicity of 2 environmentally relevant, neurotoxic PCB congeners and their OH-PCB metabolites in lactating mice and their offspring following dietary exposure of the dam. Female C57BL/6N mice (8-weeks old) were fed daily, beginning 2 weeks prior to conception and continuing throughout gestation and lactation, with 3.1 µmol/kg bw/d of racemic 2,2',3,5',6-pentachlorobiphenyl (PCB 95) or 2,2',3,3',6,6'-hexachlorobiphenyl (PCB 136) in peanut butter; controls received vehicle (peanut oil) in peanut butter. PCB 95 levels were higher than PCB 136 levels in both dams and pups, consistent with the more rapid metabolism of PCB 136 compared with PCB 95. In pups and dams, both congeners were enriched for the enantiomer eluting second on enantioselective gas chromatography columns. OH-PCB profiles in lactating mice and their offspring were complex and varied according to congener, tissue and age. Developmental exposure to PCB 95 versus PCB 136 differentially affected the expression of P450 enzymes as well as neural plasticity (arc and ppp1r9b) and thyroid hormone-responsive genes (nrgn and mbp). The results suggest that the enantioselective metabolism of PCBs to OH-PCBs may influence neurotoxic outcomes following developmental exposures, a hypothesis that warrants further investigation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".