Behavioral dynamics of medicinal signaling cells from porcine bone marrow in long-term culture
Bibliographic record
Abstract
Medicinal signaling cells (MSC) hold promise for regenerative medicine due to their ability to repair damaged tissues. However, their effectiveness can be affected by how long they are cultured in the lab. This study investigated how passage number influences key properties for regenerative medicine of pig bone marrow MSC. The medicinal signiling cells derived from pig bone marrow (BM-MSC) were cultured in D-MEM High Glucose supplemented with 15% foetal bovine serum until the 25th passage and assessed their growth, viability, ability to differentiate into different cell types (plasticity), and cell cycle activity. Our findings showed that while the cells remained viable until the 25th passage, their ability to grow and differentiate declined after the 5th passage. Additionally, cells in later passages spent more time in a resting phase, suggesting reduced activity. In conclusion, the number of passages is a critical factor for maintaining ideal MSC characteristics. From the 9th passage BM-MSC exhibit decline in proliferation, differentiation potential, and cell cycle activity. Given this, it is possible to suggest that the use of 5th passage cells is the most suitable for therapeutic applications.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".