Investigating Markers of Reprogramming Potential in Somatic Cell Lines Derived from Matched Donors
Bibliographic record
Abstract
Somatic cell biobanking and related technologies, somatic cell nuclear transfer (SCNT), and induction of pluripotent stem cells offer significant promise for wildlife conservation, but have yet to achieve optimal success. Inefficiency and variability in outcome have been linked to incomplete nuclear reprogramming, highlighting the importance of donor cell contribution. Studies show significant differences in SCNT outcome in donor cell lines within and between individuals, highlighting the necessity for a standardized characterization method to evaluate cell line reprogramming potential. Stringently standardized bovine fibroblast cell lines were generated and assessed for inter- and intraindividual variability on cellular (morphology, chromosome number, apoptotic incidence; Experiment 1) and molecular (pluripotency and epigenetic-related gene expression; Experiment 2) levels encompassing putative biomarkers of reprogramming potential. Cellular parameters were similar across cell lines. While some statistically significant differences were observed in DNMT1, DNMT3B, and HAT1, but not HDAC1, their biological relevance could not be determined with the information at hand. This study lays the foundation for understanding cellular characteristics in cultured cell lines; however, further studies are required to determine any correlation with reprogramming potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".