Acute Effects of Viral Exposure on P-Glycoprotein Function in the Mouse Fetal Blood-Brain Barrier
Bibliographic record
Abstract
BACKGROUND/AIMS: Viral infection during pregnancy is known to affect the fetal brain. The toll-like receptor (TLR)-3 is a pattern recognition receptor activated by viruses known to elicit adverse fetal neurological outcomes. The P-glycoprotein (P-gp) efflux transporter protects the developing fetus by limiting the transfer of substrates across both the placenta and the fetal blood-brain barrier (BBB). As such, inhibition of P-gp at these blood-barrier sites may result in increased exposure of the developing fetus to environmental toxins and xenobiotics present in the maternal circulation. We hypothesized that viral exposure during pregnancy would impair P-gp function in the placenta and in the developing BBB. Here we investigated whether the TLR-3 ligand, polyinosinic:polycytidylic acid (PolyI:C), increased accumulation of one P-gp substrate in the fetus and in the developing fetal brain. METHODS: Pregnant C57BL/6 mice (GD15.5) were injected (i.p.) with PolyI:C (5 mg/kg or 10 mg/kg) or vehicle (saline). [3H]digoxin (P-gp substrate) was injected (i.v.) 3 or 23h post-treatment and animals were euthanized 1h later. Maternal plasma, 'fetal-units' (fetal membranes, amniotic fluid and whole fetus), and fetal brains were collected. RESULTS: PolyI:C exposure (4h) significantly elevated maternal plasma IL-6 (P<0.001) and increased [3H]digoxin accumulation in the fetal brain (P<0.05). In contrast, 24h after PolyI:C exposure, no effect on IL-6 or fetal brain accumulation of P-gp substrate was observed. CONCLUSION: Viral infection modeled by PolyI:C causes acute increases in fetal brain accumulation of P-gp substrates and by doing so, may increase fetal brain exposure to xenobiotics and environmental toxins present in the maternal circulation.
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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 | 0.001 |
| 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".