Small RNA sequencing reveals distinct nuclear microRNAs in pig granulosa cells during ovarian follicle growth
Bibliographic record
Abstract
Nuclear small RNAs have emerged as an important subset of non-coding RNA species that are capable of regulating gene expression. A type of small RNA, microRNA (miRNA) have been shown to regulate development of the ovarian follicle via canonical targeting and translational repression. Little has been done to study these molecules at a subcellular level. Using cell fractionation and high throughput sequencing, we surveyed cytoplasmic and nuclear small RNA found in the granulosa cells of the pig ovarian antral preovulatory follicle. Bioinformatics analysis revealed a diverse network of small RNA that differ in their subcellular distribution and implied function. We identified predicted genomic DNA binding sites for nucleus-enriched miRNAs that may potentially be involved in transcriptional regulation. The small nucleolar RNA (snoRNA) SNORA73, known to be involved in steroid synthesis, was also found to be highly enriched in the cytoplasm, suggesting a role for snoRNA species in ovarian function. Taken together, these data provide an important resource to study the small RNAome in ovarian follicles and how they may impact fertility.
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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 | 0.000 | 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) | 0.001 | 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".