Establishment and validation of cell pools using primary muscle cells derived from satellite cells of pig skeletal muscle
Bibliographic record
Abstract
Primary cell cultures derived from satellite cells of skeletal muscle provide an appropriate in vitro model for proliferating myoblasts and differentiating myotubes for muscle biological research. These cell cultures may consist of harvested cells per animal or of a cell pool made of cells from several animals. However, cell pooling reduces the biological variability of the different cell donors. On the other hand, the use of cell pools offers an opportunity to use less donor tissue and to perform long-term projects with a broad spectrum of analysis and replications. In the literature, information about the donors of cell pools, the procedure used for pooling, and the characterization/validation of cell pools is often lacking. In this study, we established three cell pools consisting of M. rhomboideus or M. longissimus from ten or six piglets, each with one gender and medium birth weight. Real-time impedimetric monitoring was used to evaluate the proliferative growth behavior of myoblasts for the cell pools in comparison to their corresponding unpooled cells over a period of 72 h, with a measurement being taken every 30 min. For each of the tested cell pools, cell index, slope, and doubling time did not differ between the cell pool and the unpooled cells of the donor animals. Differentiation capacity and mRNA expression of PAX7, MYOD and MYOG remained unchanged between the cell pool and the unpooled cells. Current results support that the use of cell pools is an appropriate method to reflect the average proliferative growth behavior of unpooled cells.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".