Low levels of exosomal-miRNAs in maternal blood are associated with early pregnancy loss in cloned cattle
Bibliographic record
Abstract
Nuclear reprogramming mediated by somatic cell nuclear transfer (SCNT) has many applications in medicine. However, animal clones show increased rates of abortion and reduced neonatal viability. Herein, we used exosomal-miRNA profiles as a non-invasive biomarker to identify pathological pregnancies. MiRNAs play important roles in cellular proliferation and differentiation during early mammalian development. Thus, the aim of this study was to identify exosomal-miRNAs in maternal blood at 21 days of gestation that could be used for diagnosis and prognosis during early clone pregnancies in cattle. Out of 40 bovine-specific miRNAs, 27 (67.5%) were with low abundance in the C-EPL (Clone - Early pregnancy loss) group compared with the C-LTP (Clone - Late pregnancy) and AI-LTP (Artificial Insemination - Late pregnancy) groups, which had similar miRNAs levels. Bioinformatics analysis of the predicted target genes demonstrated signaling pathways and functional annotation clusters associated with critical biological processes including cell proliferation, differentiation, apoptosis, angiogenesis and embryonic development. In conclusion, our results demonstrate decreased exosomal-miRNAs in maternal blood at 21 days of gestation in cloned cattle pregnancies that failed to reach term. Furthermore, the predicted target genes regulated by these 27 miRNAs are strongly associated with pregnancy establishment and in utero embryonic development.
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 | 0.001 |
| 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 | 0.001 | 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".