The rat mammary gland undergoes dynamic transcriptomic and lipidomic modifications from pre-puberty to adulthood
Bibliographic record
Abstract
Mammary gland development is a complex process involving dynamic interaction between the epithelial and stromal components at different critical stages, particularly around puberty. While epithelial tissue changes are well-documented, stromal mechanisms are less understood. To address this gap, this study employed histology, lipidomic, and transcriptomic analyses to investigate molecular and cellular dynamics in the mammary gland during pre-puberty (Post Natal Day (PND21)), peri-puberty (PND46), and adulthood (PND90) in rats. The epithelial area was significantly smaller at PND21 than at PND46 and PND90, with a higher complexity at PND21 compared to PND46. Significant differences in adipocyte number and size were observed between PND21, PND46, and PND90. Transcriptomic analysis revealed that 1563 genes changed significantly between PND21 and PND46, with only 14 genes altered between PND46 and PND90. Enrichment analyses indicated dynamic regulation of pathways related to proliferation, differentiation, lipid metabolism, and immune responses. In lipidomic analysis, 29/43 and 7/43 fatty acids differed significantly between PND21 - PND46 and PND46 - PND90, respectively. These results suggest that mammary gland development involves complex interactions between metabolic demands, hormonal regulation, and immune responses.
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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".