Genome wide expression analysis of circular RNAs in mammary epithelial cells of cattle revealed difference in milk synthesis
Bibliographic record
Abstract
Milk is an excellent source of nutrients for humans. Therefore, in order to enhance the quality and production of milk in cattle, it is interesting to examine the underlying mechanisms. A number of new investigations and research have found that, circRNA; a specific class of non-coding RNAs, is linked with the development of mammary gland and lactation. In the present study, genome wide identification and expression of the circRNAs in mammary epithelial cells of two distinct cattle breeds viz Jersey and Kashmiri at peak lactation was conducted. We reported 1554 and 1286 circRNA in Jersey and Kashmiri cattle, respectively, with 21 circRNAs being differentially expressed in the two breeds. The developmental genes of the established differentially expressed circRNAs were found to be largely enriched in antioxidant activity, progesterone, estradiol, lipid, growth hormone, and drug response. Certain pathways like MAPK, IP3K and immune response pathways were found significantly enriched in KEGG analysis. These results add to our understanding of the controlling mechanisms connected with the lactation process, as well as the function of circRNAs in bovine milk synthesis. Additionally, the comparative analysis of differentially expressed circRNAs showed significant conservation across different species.
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 | 0.001 |
| 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) | 0.001 | 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".