Neonatal Experiences Differentially Influence Mammary Gland Morphology, Estrogen Receptor α Protein Levels, and Carcinogenesis in BALB/c Mice
Bibliographic record
Abstract
Prevention of breast cancer can be achieved with a better understanding of the factors contributing to normal breast development. Because the breast develops postnatally, alterations in the development and lifetime activity of the neuroendocrine system may set up an environment that increases cancer risk. The present study examined how two neonatal experiences over the first 3 weeks of life influence normal and malignant mammary gland development in female BALB/c mice. Following puberty, both brief (15 minutes) and prolonged (4 hours) daily maternal separations of newborn mice accelerated mammary gland development relative to nonseparated mice. Despite similar mammary gland morphologies between mice exposed to these two neonatal separation experiences, only mice exposed to prolonged maternal separation bouts showed a higher incidence and faster onset of mammary tumorigenesis following adulthood carcinogen [7,12-dimethylbenz(a)anthracene] administration. Molecular analysis of estrogen receptor α (ERα) and p53, two proteins that have been implicated in breast cancer, revealed that for mice exposed to prolonged neonatal maternal separation bouts, mammary gland ERα protein levels were upregulated in a transcription-independent manner. On the other hand, p53 expression in mammary glands of adult mice was not differentially influenced by neonatal experiences. Our findings show that chronic, moderate psychosocial stress during the neonatal period increases the expression of ERα protein and promotes mammary tumorigenesis in adulthood.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".