Transcriptome MicroRNA Profiling of Bovine Mammary Glands Infected with Staphylococcus aureus
Bibliographic record
Abstract
MicroRNAs are small non-coding RNA molecules that are important regulators of gene expression at the post-transcriptional level. miRNAs impact the processes of cell proliferation, differentiation and apoptosis. Thus, the regulation of miRNA expression profiles associated with mastitis will be conducive for its control. In this study, Staphylococcus aureus (S. aureus) was administered to the mammary gland of Chinese Holstein cows to construct a bacteria-type mastitis model. Total RNA was isolated from bovine mammary gland tissue samples from the S. aureus-induced mastitis group and controls. miRNAs were analyzed using Solexa sequencing and bioinformatics processing for the experimental group and control group. Two miRNA libraries were constructed respectively. A total of 370 known bovine miRNAs and 341 novel mi RNAs were detected for the S. aureus and 358 known bovine miRNAs and 232 novel miRNAs for control groups. A total of 77 miRNAs in the S. aureus group showed significant differences compared to the control group. GO (Gene Ontology) analysis showed these target genes were involved in the regulation of cells, binding, etc., while KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis showed that these genes were enriched in endocytosis, and olfactory transduction pathways involved in cancer. These results provide an experimental basis to reveal the cause and regulatory mechanism of mastitis and also suggest the potential of miRNAs to serve as biomarkers for the diagnosis of mastitis in dairy cows.
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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) | 0.000 | 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 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".