Soy protein and its effects on bone metabolism in young and old female rats fed low dietary calcium
Bibliographic record
Abstract
Soy protein (SP) and soy isoflavones may slow bone loss. While many studies have examined the effects of feeding SP and/or isoflavones on ovariectomized rats, a model of postmenopausal bone loss, little has been done with animal models of Ca deficiency. Ca deficiency decreases bone growth and causes bone loss, and serves as a model of osteoporosis. In this study we examined the effects of feeding SP or SP plus isoflavones on bone metabolism in female weanling and retired breeder (RB) rats fed low levels of Ca. Bone mineral density (BMD), bone mineral content (BMC), bone growth parameters, and biochemical markers of bone metabolism were measured. Young rats fed SP had significantly smaller reductions in BMD and BMC after consuming a low Ca diet compared to a casein‐based diet. Isoflavones had no further benefits. BMD, BMC and bone growth parameters of RB rats were unaffected by SP. However, SP showed positive effects on bone turnover in both young and RB rats as determined by bone resorption markers. Feeding SP positively affects bone metabolism and minimizes, but does not fully reverse, the negative effects associated with low Ca intakes in young rats. These data merit further investigation on the effects of SP on bone growth, especially when Ca intake is inadequate. (Support: Bureau of Nutritional Sciences, Health Canada).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".