Exposure to Flaxseed or Purified Lignan During Lactation Influences Rat Mammary Gland Structures
Bibliographic record
Abstract
Previous investigation demonstrated that feeding a 10% flaxseed (10F) diet during pregnancy and lactation enhanced the differentiation of highly proliferative terminal end bud (TEB) structures of rat mammary gland into less proliferative alveolar buds and lobules. From this study, it was hypothesized that the lignan component in flaxseed mediated the observed effects. Because mammary glands with more TEBs are more susceptible to carcinogens, exposure to flaxseed during early postnatal life may reduce the risk of developing mammary cancer. Our objectives were to elucidate whether exposure to flaxseed during lactation only and during pregnancy and lactation can similarly influence the differentiation of mammary gland structures and also to identify whether the lignan component of flaxseed is the biologically active agent. Offspring were exposed to a 10F diet or a dose of purified lignan equivalent to that in a 10F diet (10S) during lactation only or from lactation to postnatal Day 50. Compared with controls, exposure to 10F or 10S during lactation only or from lactation to postnatal Day 50 reduced the number of TEBs and resulted in a rise in the number of alveolar buds. In conclusion, exposure to flaxseed or its purified lignan during lactation is a critical period in which mammary gland development may be promoted by enhancing the differentiation of the mammary gland structures. However, continuous exposure, particularly to purified lignans, resulted in the most differentiation of the mammary gland. The next step is to determine whether the changes in mammary gland structures are chemopreventive in rats challenged with a carcinogen.
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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".