Enzymatic Dissociation and Culture of Normal Human Mammary Tissue to Detect Progenitor Activity
Bibliographic record
Abstract
Normal human mammary tissue is composed of a glandular epithelium embedded within a fibrous and fatty stroma. Collagenase and hyaluronidase digestion of normal reduction mammoplasty specimens followed by differential centrifugation yields a suspension of single cells and cell aggregates that contain elements of the terminal ductal lobular units and stromal components of the mammary gland. The terminal ductal lobular units (TDLU) can be further dissociated to complete viable single-cell suspensions by treatment with trypsin, dispase II, and deoxyribonuclease I. These suspensions are suitable for cell separation and analysis in culture. Such studies indicate the existence of biologically distinct subpopulations of luminal-restricted, myoepithelial-restricted, and bipotent mammary epithelial progenitors detected by their ability to generate colonies of the corresponding progeny types in serum-free cultures. This review summarizes the methodology of the techniques required to generate and characterize the colonies obtained in vitro from these progenitors, as well as the special considerations and potential pitfalls associated with performing these protocols.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".