High throughput screening reveals no significant changes in protein synthesis, processing, and degradation machinery during passaging of mesenchymal stem cells
Bibliographic record
Abstract
Increasing reports of successful and safe application of bone marrow derived mesenchymal stem cells (BM-MSCs) for cell therapy are pouring in from numerous studies. However poor survival of transplanted cells in the recipient has impaired the benefits of BM-MSCs based therapies. Therefore cell product preparation procedures pertaining to MSC therapy need to be optimized to improve the survival of transplanted cells. One of the important ex vivo procedures in the preparation of cells for therapy is passaging of BM-MSCs to ensure a suitable number of cells for transplantation, which may affect the turnover of proteins involved in regulation of cell survival and (or) death pathways. In the current study, we investigated the effect of an increase in passage number of BM-MSCs in cell culture on the intracellular protein turnover (protein synthesis, processing, and degradation machinery). We performed proteomic analysis of BM-MSCs at different passages. There was no significant difference observed in the ribosomal, protein processing, and proteasomal pathways related proteins in BM-MSCs with an increase in passage number from P3 to P7. Therefore, expansion of MSCs in the cell culture in clinically relevant passages (Passage 3-7) does not affect the quality of MSCs in terms of intracellular protein synthesis and turnover.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".