Selection of Reference miRNAs for Relative Quantification in Buffalo ( <i>Bubalus bubalis</i> ) Blastocysts Produced by Hand-Made Cloning and <i>In Vitro</i> Fertilization
Bibliographic record
Abstract
Very low birth rate and a high incidence of abnormalities in offspring born from cloned embryos, which have limited the application of cloning technology on a wide scale, are believed to be because of incomplete or aberrant nuclear reprogramming. MicroRNAs (miRNAs) are involved in regulating a wide range of biological processes including reprogramming and embryonic development. Selection of suitable reference miRNAs is critical for normalization of data for accurate relative quantification of miRNAs by quantitative real-time polymerase chain reaction (qRT-PCR), which is currently the most widely used technique for quantifying miRNAs. This study was aimed at identification of reference miRNAs suitable for normalization of qRT-PCR data from blastocyst-stage buffalo embryos produced by handmade cloning and in vitro fertilization (IVF). RNA isolated from cloned and IVF blastocysts was subjected to next-generation sequencing based on which, 12 highly and most consistently expressed miRNAs, which included miR-92a, miR-423, miR-151, Let-7a, miR-103a, miR-93, miR-16b, miR-25, miR-30e, miR-101, miR-127, and miR-197, were selected as candidates for identification of suitable reference miRNAs using three statistical algorithms namely geNorm, NormFinder, and BestKeeper. Based on consensus of the three algorithms, the combination of miRNAs found to be suitable as reference miRNAs were miR-127 and miR-103 for IVF blastocysts; miR-92a and miR-103 for cloned blastocysts, and miR-103, miR-423, and miR-93 across both IVF and cloned blastocysts. The data of this study can be very useful in miRNA expression analysis of blastocyst-stage cloned and IVF embryos.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".