Cellular Turnover in the Mammary Gland Is Correlated with Systemic Levels of Progesterone and Not 17β-Estradiol During the Estrous Cycle1
Bibliographic record
Abstract
Adult mammary tissue has been considered "resting" with minimal morphological change. Here, we reveal the dynamic nature of the nulliparous murine mammary gland. We demonstrate specific changes at the morphological and cellular levels, and uncover their relationship with the murine estrous cycle and physiological levels of steroid hormones. Differences in the numbers of higher-order epithelial branches and alveolar development led to extensive mouse-to-mouse mammary variations. Morphology (assigned grades 0-3) ranged from a complete lack of alveoli to the presence of numerous alveoli emanating from branches. Morphological changes were driven by epithelial proliferation and apoptosis, which differed between ductal versus alveolar structures. Proliferation within alveolar epithelium increased as morphological grade increased. Extensive alveolar apoptosis was restricted to tissue exhibiting grade 3 morphology, and was approximately 14-fold higher than at all other grades. Epithelial proliferation and apoptosis exhibited a positive relationship with serum levels of progesterone, but not with 17beta-estradiol. Compared with other estrous stages, diestrus was unique in that the morphological grade, epithelial proliferation, apoptosis, and progesterone levels all peaked at this stage. The regulated tissue remodeling of the mammary gland was orchestrated with mRNA changes in specific matrix metalloproteinases (MMP-9 and MMP-13) and specific tissue inhibitors of metalloproteinases (TIMP-3 and TIMP-4). We propose that the cyclical turnover of epithelial cells within the adult mammary tissue is a sum of spatial and functional coordination of hormonal and matrix regulatory factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".