High‐dose riboflavin treatment reduces the levels of BCRP‐transported cimetidine into the milk
Bibliographic record
Abstract
Motherˈs milk provides a multitude of benefits to the offspring. However, drugs and toxins that are transferred into breast milk may pose a risk to the nursing infant. The Breast Cancer Resistance Protein (BCRP) is known to actively transport drugs (e.g. cimetidine) and toxins (e.g. PhIP) into breast milk. BCRP also transport nutrients such as riboflavin into breast milk and together with recently identified riboflavin transporters (RFTs), may provide a mechanism for riboflavin secretion into breast milk. It is currently not known if RFTs are expressed in the mammary gland. Our objective was to characterize BCRP and RFTs expression in the mammary gland of FVB/N mice, and to investigate a potential strategy to decrease BCRP‐transported xenobiotics excretion into the milk using a high‐dose riboflavin intervention. RFTs and BCRP expression was upregulated in the mammary gland of lactating mice. An intravenous injection of 5 μg/g body weight of riboflavin that enhanced the levels of riboflavin in milk and plasma by 3.1‐ and 8.9–fold, respectively, significantly reduced the levels of BCRP‐transported 3H‐cimetidine in milk. Also, cimetidine's milk‐to‐plasma ratio was significantly reduced (1.4 fold: p<0.05). This study demonstrates the use of riboflavin to exploit the function of mammary BCRP in order to reduce xenobiotic section into breast milk. Supported by CIHR, NSERC, Restracomp and U of T Fellowship.
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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 | 0.001 | 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".