Effects of Neonatal Exposure to Genistein on Bone Metabolism in Mice at Adulthood
Bibliographic record
Abstract
Early, short‐term exposure to estrogen in female mice has positive effects on the skeleton at adulthood. Infants can be exposed to high levels of genistein, with potential estrogen‐like activity, by consuming soy‐based infant formula. The objective of this study was to determine whether neonatal exposure of male and female mice to genistein during early development resulted in a higher bone mineral density (BMD) and greater resistance to fracture at adulthood. Male and female CD‐1 mice (n=4‐14/group) were randomized to control (CON) (corn oil, s.c.), diethylstilbestrol (DES) (2 μg/pup/day, s.c.) or genistein (GEN) (4 μg/pup/day, s.c.) from day 1 through 5 of life. At 21 days of age, pups were weaned and studied until four months of age at which time tissues were collected. Among females, femur (p=0.016) and lumbar vertebrae (LV1‐LV4) (p<0.001) BMD were higher among DES and GEN groups compared to CON group. Importantly, the higher LV1‐LV4 BMD was associated with stronger vertebrae that were more resistant to fracture as the peak load of LV3 (p=0.012) was higher in the GEN and DES groups compared to CON group. In males, DES and GEN had divergent effects on femur and lumbar vertebrae BMD, and peak load. In conclusion, early exposure to GEN has positive effects in the female skeleton, likely due to estrogenic effects, while the male skeleton does not benefit from early exposure to GEN. Further studies are required to investigate the mechanisms by which genistein exposure during the earliest stages of postnatal life modulates BMD and bone strength at adulthood. Funded by the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".