Bioprocessing strategies for the large-scale production of human mesenchymal stem cells: a review
Bibliographic record
Abstract
Human mesenchymal stem cells (hMSCs), also called mesenchymal stromal cells, have been of great interest in regenerative medicine applications because of not only their differentiation potential but also their ability to secrete bioactive factors that can modulate the immune system and promote tissue repair. This potential has initiated many early-phase clinical studies for the treatment of various diseases, disorders, and injuries by using either hMSCs themselves or their secreted products. Currently, hMSCs for clinical use are generated through conventional static adherent cultures in the presence of fetal bovine serum or human-sourced supplements. However, these methods suffer from variable culture conditions (i.e., ill-defined medium components and heterogeneous culture environment) and thus are not ideal procedures to meet the expected future demand of quality-assured hMSCs for human therapeutic use. Optimizing a bioprocess to generate hMSCs or their secreted products (or both) promises to improve the efficacy as well as safety of this stem cell therapy. In this review, current media and methods for hMSC culture are outlined and bioprocess development strategies discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".