Validation of reference genes for use in untreated bovine fibroblasts
Bibliographic record
Abstract
Proper normalization of RT-qPCR data is pivotal to the interpretation of results and accuracy of scientific conclusions. Though different approaches may be taken, normalization against multiple reference genes is now standard practice. Genes traditionally used and deemed constitutively expressed have demonstrated variability in expression under different experimental conditions, necessitating the proper validation of reference genes prior to utilization. Considering the wide use of fibroblasts in research and scientific applications, it is imperative that suitable reference genes for fibroblasts of different animal origins and conditions be elucidated. Previous studies on bovine fibroblasts have tested limited genes and/or samples. Herein, we present an extensive study investigating the expression stability of 16 candidate reference genes across 7 untreated bovine fibroblast cell lines subjected to controlled conditions. Data were analysed using various statistical tools and algorithms, including geNorm, NormFinder, BestKeeper, and RefFinder. A combined use of GUSB and RPL13A was determined to be the best approach for data normalization in untreated bovine fibroblasts.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".