Effects of soybean isoflavones on reproductive parameters in Chinese mini-pig boars
Bibliographic record
Abstract
BACKGROUND: Soybean isoflavones are structurally similar to mammalian estrogens and therefore may act as estrogen agonists or antagonists. However, it has not been determined if they have any negative effects on reproductive parameters in male livestock. Therefore, the objective of this study was to evaluate the effects of soybean isoflavones on male reproduction using Chinese mini-pig boars as a model. Fifty Xiang boars were randomly divided into five groups and fed diets containing 0, 125, 250, or 500 ppm soybean isoflavones or 0.5 ppm diethylstilbestrol for 60 days. RESULTS: Dietary supplementation with 250 ppm of soy isoflavones markedly increased the testis index (P < 0.05), fructose content (P < 0.05), and α-glycosidase content in testicular tissue (P < 0.01), as well as increased the number of viable germ cells (P < 0.01) and the level of Bcl-2 protein (P < 0.01). However, 500 ppm of soybean isoflavones significantly reduced both testis and epididymis indexes (P < 0.05) and lactate dehydrogenase levels (P < 0.01), as well as reduced serum LH and testosterone levels (P < 0.05). High levels of soybean isoflavones also increased malondialdehyde levels (P < 0.05), as well as increased the numbers of early and late apoptotic germ cells (P < 0.01) and the level of Bax proteins (P < 0.05) in the testis. CONCLUSIONS: The results of this study indicate that consumption of soy isoflavones at dietary levels up to 250 ppm did not adversely affect reproductive parameters in Chinese mini-pig boars whereas higher levels of soy isoflavones may adversely affect male reproduction.
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 | 0.000 | 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 | 0.001 |
| 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".