Lactation stage‐dependent expression of transporters in rat whole mammary gland and primary mammary epithelial organoids
Bibliographic record
Abstract
Since solute carrier (SLC) and ATP-binding cassette (ABC) transporters play pivotal roles in the transport of both nutrients and drugs into breast milk, drug-nutrient transport interactions at the lactating mammary gland are possible. Our purpose was to characterize lactation stage-dependent changes in transporter expression in rat mammary gland and isolated mammary epithelial organoids (MEO) to provide additional insight for the safe use of maternal medications during breastfeeding. We used quantitative reverse transcription-polymerase chain reaction to assess the temporal expression patterns of SLC and ABC transporters in rat mammary gland and isolated MEO at different stages of lactation. In whole mammary gland five distinct patterns of expression emerged relative to late gestation: (i) decreasing throughout lactation (Mdr1a, Mdr1b, Mrp1, Octn2, Ent2, Ent3, Ncbt2, Mtx1); (ii) prominent increase in early lactation, which may remain elevated or decline with advancing lactation (Octn1, Cnt2, Cnt3, Ent1, Pept1, Pept2); (iii) constant but decreasing later in lactation (Octn3, Dmt1); (iv) increasing until mid-to-late lactation (Oct1, Cnt1); and (v) prominent increase late in lactation (Ncbt1). In isolated MEO (an enriched source of mammary epithelial cells) major differences in expression patterns were noted for Octn3, Ncbt1, and Mtx1, but otherwise were reasonably similar with the whole mammary gland. In conclusion our study augments existing data on transporter expression in the lactating mammary gland. These data should facilitate investigations into lactation-stage dependent changes in drug or nutrient milk-to-serum concentration ratios, the potential for drug- or disease-transporter interactions, and mechanistic studies of transporter function in the lactating mammary gland.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".