Quantification of Housekeeping Transcript Levels During the Development of Bovine Preimplantation Embryos1
Bibliographic record
Abstract
In mammals, the study of gene expression in the preimplantation embryo has been difficult because the standard procedures used to quantify mRNA generally require large amounts of starting material. The development of protocols using different quantitative strategies generally involving the polymerase chain reaction (PCR) has provided new tools for exploration of gene expression in preimplantation embryos. However, the use of an internal standard, often referred as a housekeeping gene, is essential to normalize the mRNA levels. RNA levels of eight housekeeping genes were quantified using real time PCR throughout the preimplantation period of the bovine embryo to find the most suitable gene to be used as standard. Histone H2a was the best internal standard because the transcript levels were constant across the preimplantation period. Linear amplification of antisense RNA using the T7 promotor for in vitro transcription of the entire RNA pool was evaluated as a suitable way to preamplify the starting material prior to quantification and was effective in providing accurate RNA abundance profiles throughout the preimplantation period. However, the amplification appears to be template dependent because the amplification factors were higher for some genes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".