Identification and Preliminary Analysis of Granulosa Cell Biomarkers to Predict Oocyte In Vitro Maturation Outcome in the Southern White Rhinoceros (Ceratotherium simum simum)
Bibliographic record
Abstract
In recent years, biomarkers in granulosa cells (GC) have been determined and associated in several species with oocyte maturation, in vitro fertilization success, and embryo development outcomes. The identification of biomarkers of oocyte competence can aid in improving assisted reproductive technologies (ARTs) in the southern white rhino (SWR). This study aimed to identify biomarkers present in SWR GC associated with oocytes that either did or did not mature in vitro. We evaluated follicle development (FD), meiotic competence (MC), cell death and atresia (CDA), and embryonic genome activation (EGA). Our objective was to design biomarkers to predict oocyte in vitro maturation results in the SWR. RNA was isolated from GC obtained during ovum pick up (OPU) for qPCR analysis. Overall, 22 genes were assessed, and nine were differentially expressed between GC from oocytes that did or did not mature in vitro (FD-GDF9 and mTOR; MC-GGPS1, JMY, and NPR2; CDA-COL4A1, MACIR, and TMPO; EGA-NFYA). From these data, we determined that GC can be used as a predictor for oocyte in vitro maturation outcome in the SWR. Our results provide crucial information needed to improve in vitro maturation and ARTs in this species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".