Faculty Opinions recommendation of Placental P-glycoprotein and breast cancer resistance protein: influence of polymorphisms on fetal drug exposure and physiology.
Bibliographic record
Abstract
Recent studies have illustrated the importance of placental drug transport proteins, such as P-glycoprotein (Pgp) and breast cancer resistance protein (BCRP) in limiting fetal exposure to drugs and toxins. Moreover, increasing evidence supports a role for Pgp and BCRP in the normal development and physiological function of the placenta. Several single nucleotide polymorphisms (SNPs) in the genes encoding Pgp and BCRP have been described and are associated with altered protein expression, transporter activity, and clinical outcome in studies focusing on tissues other than the placenta. This review aims to summarize current research regarding the association between these polymorphisms and expression and function in the placenta. The influence of these genotypes on fetal drug exposure and altered placental physiology or development is also presented. To date, evidence suggests that SNPs in both ABCB1 and ABCG1 can alter expression of their respective protein; however, the functional significance of these polymorphisms is less clear. An understanding of this genotype-phenotype relationship will allow for prediction of susceptible or favorable genotypes in order to personalize medication choices to minimize fetal exposure to teratogens, or to maximize pharmacological therapy to the fetus.(c) 2010 Elsevier Ltd. All rights reserved. PMID: 20347140 Funding information This work was supported by: Canadian Institutes of Health Research, Canada
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".