Breast cancer resistance protein (Bcrp/Abcg2) is selectively modulated by lipopolysaccharide (LPS) in the mouse yolk sac
Bibliographic record
Abstract
Bacterial infection alters placental ABC transporters expression. These transporters provide fetal protection against circulating xenobiotics and environmental toxins present in maternal blood. We hypothesized that lipopolysaccharide (LPS-bacterial mimic) alters the yolk sac morphology and expression of key ABC transporters in a gestational-age dependent manner. Yolk sac samples from C57BL/6 mice were obtained at gestational ages (GD) 15.5 and GD18.5, 4 or 24 h after LPS exposure (150ug/kg; n = 8/group). Samples underwent morphometrical, qPCR and immunohistochemistry analysis. The volumetric proportions of the histological components of the yolk sac did not change in response to LPS. LPS increased Abcg2 expression at GD15.5, after 4 h of treatment (p < 0.05). No changes in Abca1, Abcb1a/b, Abcg1, Glut1, Snat1, Il-1β, Ccl2 and Mif were observed. Il-6 and Cxcl1 were undetectable in the yolk sac throughout pregnancy. Abca1, breast cancer resistance protein (Bcrp, encoded by Abcg2) and P-glycoprotein (P-gp/ Abcb1a/b) were localized in the endodermal (uterine-facing) epithelium and to a lesser extent in the mesothelium (amnion-facing), whereas Abca1 was also localized to the endothelium of the yolk sac blood vessels. LPS increased the labeling area and intensity of Bcrp in the yolk sac's mesothelial cells at GD15.5 (4 h), whereas at GD18.5, the area of Bcrp labeling in the mesothelium (4 and 24 h) was decreased (p < 0.05). Bacterial infection has the potential to change yolk sac barrier function by affecting Bcrp and Abcg2 expression in a gestational-age dependent-manner. These changes may alter fetal exposure to xenobiotics and toxic substances present in the maternal circulation and in the uterine cavity.
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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".