The Effect of Tobacco Use on the Expression of Placental Transporters in Alaska Native Women
Bibliographic record
Abstract
Prenatal tobacco use among Alaska Native (AN) women has decreased substantially over the past two decades. Previous research suggests that providing AN women with feedback regarding fetal exposure to tobacco may further promote cessation. Transporters in the placenta regulate fetal exposure to nutrients and xenobiotics, including compounds associated with tobacco use. We examined whether prenatal tobacco use impacts transporter expression in the placenta, and whether this is influenced by fetal sex, degree of tobacco exposure, or transporter genotype. At delivery, we obtained placental samples from AN research participants who smoked cigarettes, used commercial chew or iqmik (oral tobacco), or did not use tobacco during pregnancy. Transporter expression was evaluated using qRT-PCR and Western blotting and tested for correlations between transcript levels and urinary biomarkers of tobacco use. The impact of BCRP/ABCG2 and OATP2B1/SLCO2B1 genotypes on protein expression was also examined. Oral tobacco use was associated with decreased P-gp and increased MRP1, MRP3, LAT1, and PMAT mRNA expression. Transcript levels of multiple transporters significantly correlated with tobacco biomarkers in maternal and fetal urine. In women carrying male fetuses, both smoking and oral tobacco were associated with decreased P-gp. Oral tobacco was also associated with decreased LAT1 in women carrying female fetuses. BCRP and OATP2B1 genotypes did not appear to impact protein expression. In conclusion, prenatal tobacco use is associated with altered expression of multiple placental transporters which differs by fetal sex. As transcript levels of multiple transporters were significantly correlated with tobacco use biomarkers, eliminating prenatal tobacco use should alleviate these changes.
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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 | 0.001 |
| 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 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".