Differences of the Fertility Potential between Buffaloes (Bubalus bubalis) and Cattle (Bos indicus): The Role of Antimullerian Hormone (AMH)
Bibliographic record
Abstract
For years the study of the differences in reproduction between bovines have been restricted to describe the consequences not the causes, it is very easy to find differences in parameters such as embryo/oocyte morphology, metabolism, cleavage rate, but it is quite difficult to find papers trying to explain the reason of this differences and it is not possible to identify their influence in the reproductive parameters and answer to reproductive biotechnologies. The idea that the quantity of follicles and oocytes in ovaries impacts on fertility is a long-held tenet in reproductive biology (46), Follicle formation occurs during fetal life in ruminants and primates. The establishment of the pool of primordial follicles is critical to a female’s reproductive success, but very little is known about how this important developmental process is regulated. It has been reported is has been reported in buffaloes the effect of season in the gene expression of oocytes and follicles (47) .However, until now very few studies has been attempted to evaluate this fundamental hypothesis, it is possible to think that animals with low follicle count such buffaloes has lower fertility than cattle but this must be demonstrated. The aim of this review is to present evidence related to the differences in reproductive potential in two closely related bovines: buffaloes (Bubalus bubalis) and cattle (Bos taurus and Bos indicus), with special emphasis in the role of antimullerian hormone (AMH) and discuss their possible role in the application of reproductive biotechnologies.
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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 | 0.001 | 0.001 |
| 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".