Granulosa cells provide transcriptomic information on ovarian follicle dynamics in southern white rhinoceros
Bibliographic record
Abstract
Much remains unknown about the reproductive physiology of southern white rhinoceros (SWR) and the effect of ovarian stimulation prior to ovum pickup (OPU) have not been fully elucidated. Granulosa cells (GC) provide valuable insight into follicle growth and oocyte maturation status. The goals of this study were to evaluate transcriptomic changes in GC from three stages of follicle development and to identify biomarkers possibly associated with follicular growth and maturation as a result of ovarian stimulation. GC collected from SWRs following OPU were assigned stages based upon follicle size. Total RNA was isolated, and cDNA libraries were prepared and sequenced on a NovaSeq 6000. All bioinformatics analyses were performed utilizing the Galaxy web platform. Reads were aligned to CerSimCot1.0, and the manual curation was performed with EquCab3.0. Overall, 39,455 transcripts (21,612 genes) were identified across follicle stages, and manual curation yielded a 61% increase in gene identification from the original annotation. Granulosa cells from preovulatory follicles expressed the highest number of unique transcripts. The following seven biomarkers were determined based upon cluster analysis and patterns of expression: COL1A1, JMY, FBXW11, NRG1, TMPO, MACIR and COL4A1. These data can be used to potentially evaluate the effects of different ovarian stimulation protocols on follicle dynamics, improve OPU results, and support conservation efforts in this species.
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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 | 0.000 |
| 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) | 0.001 | 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".