Regulation of gene expression in human mammary epithelium: effect of breast pumping
Bibliographic record
Abstract
Little is known of the molecular regulation of human milk production because of limitations in obtaining mammary tissue from lactating women. Our objectives were to evaluate whether RNA isolated from breast milk fat globules (MFGs) could be an alternative to mammary biopsies and to determine whether intense breast pumping, which increases prolactin (PRL) secretion, will upregulate alpha-lactalbumin (alpha-LA, a major determinant of lactose synthesis) transcription. RNA was isolated from MFG and transcripts of interest were identified and quantitated by real-time RT-PCR using an external standard for normalization. In addition, we performed microarray studies to determine MFG RNA gene expression profile. Ten lactating women were studied using two protocols: protocol A with intense pumping from 0800 to 0814 h followed by short pumping and protocol B with intense pumping from 1200 to 1214 h preceded by short pumping. Plasma PRL and MFG alpha-LA mRNA expression were measured. During protocol A, plasma PRL (61+/-7-248+/-43 mug/l by 14 min) and alpha-LA (3.5+/-0.9 fold by 6 h; P<0.03) increased. During protocol B, PRL gradually increased over 4 h from 69+/-14 to 205+/-28 mug/l, and further to 329+/-23 mug/l by 12 min of intense pumping; alpha-LA mRNA expression did not increase significantly. We conclude that MFGs provide a unique source to study the in vivo regulation of gene expression in mammary epithelial cells. alpha-LA mRNA is abundant in the MFG and its expression may be regulated by hormonal and temporal factors.
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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 | 0.000 |
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".