A Novel Method for Growing Human Breast Epithelium in Vivo Using Mouse and Human Mammary Fibroblasts
Bibliographic record
Abstract
A novel system is described for studying the growth of normal human mammary epithelium in vivo as grafts in athymic nude mice. The key feature of this model is reconstitution of the epithelial-stromal interactions required for normal growth and differentiation of the human mammary epithelium, which produces ducts that are comparable to those in the normal human mammary gland. Human breast epithelial organoids were combined with mammary fibroblasts from mouse or human origin in collagen gels, which were subsequently transplanted under the renal capsule of female nude mice hosts. The resulting grafts showed an increase in the ductal density compared with that observed previously. These ducts expressed appropriate markers for luminal and myoepithelial cells and steroid receptors. Treatment of the host with diethylstilbestrol or estradiol and progesterone significantly increased the number of ducts observed and increased cell proliferation. The grafts also displayed production of beta-casein and milk fat globule membrane protein when the hosts were allowed to become pregnant. This model allows for a variety of epithelial and stromal cells to be used in combination, which would aid in understanding key factors that regulate normal human mammary gland development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".