Investigating the expression of fertility-regulating LncRNAs in multiparous and uniparous Shal ewe's ovaries
Bibliographic record
Abstract
Sheep is the primary source of animal protein in Iran. Birth type is one of the significant features that determine total meat output. Little is known about how long non-coding RNAs (LncRNAs) affect litter size. The purpose of this research is to investigate the DE–LncRNAs in ovarian tissue between multiparous and uniparous Shal ewes. Through bioinformatics analyses, LncRNAs with variable expression levels between ewes were discovered. Target genes were annotated using the DAVID database, and STRING and Cytoscape software were used to evaluate their interactions. The expression levels of 148 LncRNAs were different in the multiparous and uniparous ewe groups (false discovery rate (FDR) < 0.05). Eight biological process terms, nine cellular component terms, 10 molecular function terms, and 38 KEGG pathways were significant (FDR < 0.05) in the GO analysis. One of the most significant processes impacting fertility is mitogen-activated protein kinase (MAPK) signaling pathway, followed by oocyte meiosis, gonadotropin-releasing hormone signaling pathway, progesterone-mediated oocyte maturation, oxytocin signaling pathway, and cAMP signaling pathway. ENSOARG00000025710, ENSOARG00000025667, ENSOARG00000026034, and ENSOARG00000026632 are LncRNAs that may affect litter size and fertility. The most crucial hub genes include MAPK1, BRD2, GAK, RAP1B, FGF2, RAP1B, and RAP1B. We hope that this study will encourage researchers to further investigate the effect of LncRNAs on fertility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".