Early Pregnancy Diagnosis by Serum Progesterone and Ultrasound in Sheep Carrying Somatic Cell Nuclear Transfer‐Derived Pregnancies
Bibliographic record
Abstract
Early pregnancy diagnosis and monitoring play an important role following embryo transfer in sheep. The aims of the current study were to investigate (i) the pattern of serum progesterone profiles in sheep carrying somatic cell nuclear transfer (SCNT)-derived (clone) pregnancies, and (ii) the frequency of pregnancy loss during development following SCNT embryo transfer. Sheep SCNT embryos were made using standard nuclear transfer techniques. Day 7 embryos were surgically transferred to oestrus-synchronized recipients (n = 27). As a control, normal fertile ewes (n = 12) were bred by natural breeding. Serum was collected from all the ewes on the day of estrus (day 0 sample), 7 days post-estrus (day 7 sample) and 19 days post-estrus (day 19 sample) and every 10 days thereafter until lambing or pregnancy loss occurred. Serum progesterone (P4) was assessed using enzyme immunoassay. Pregnancy was confirmed by ultrasound scanning on day 35 of pregnancy followed by subsequent scanning every 10 days. In control ewes, pregnancy rate on day 35 was 83.3% (10/12), whereas in the ewes that received SCNT embryos, it was 22.2% (6/27; p < 0.05). The day 45 pregnancy rate in the control ewes was 83.3%, whereas in the SCNT embryo recipients it was 11.0% (p < 0.05). Hormone analysis revealed that SCNT embryo recipients exhibited a significantly lower P4 profiles at different time points in pregnancy compared to controls (p < 0.05). This study highlights the use of serum progesterone in combination with ultrasound for the investigation of embryo loss and crucial times during development of normal and SCNT embryos in sheep. Further, the serum P4 levels directly reflect the degree of placental development in these two groups.
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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 | 0.001 |
| 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) | 0.000 | 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".