Production of Mesenchymal Progenitor Cell-Derived Extracellular Vesicles in Suspension Bioreactors for Use in Articular Cartilage Repair
Bibliographic record
Abstract
Mesenchymal progenitor cells (MPCs) have shown promise initiating articular cartilage repair, with benefits largely attributed to the trophic factors they secrete. These factors can be found in the conditioned medium (CM) collected from cell cultures, and it is believed that extracellular vesicles (EVs) within this CM are at least partially responsible for MPC therapeutic efficacy. This study aimed to examine the functionality of the EV fraction of CM compared to whole CM obtained from human adipose-derived MPCs in an in vivo murine cartilage defect model. Mice treated with whole CM or the EV fraction demonstrated an enhanced cartilage repair score and type II collagen deposition at the injury site compared to saline controls. We then developed a scalable bioprocess using stirred suspension bioreactors (SSBs) to generate clinically relevant quantities of MPC-EVs. Whereas static monolayer culture systems are simple to use and readily accessible, SSBs offer increased scalability and a more homogenous environment due to constant mixing. This study evaluated the biochemical and functional properties of MPCs and their EV fractions generated in static culture versus SSBs. Functionality was assessed using in vitro MPC chondrogenesis as an outcome measure. SSBs supported increased MPC expression of cartilage-specific genes, and EV fractions derived from both static and SSB culture systems upregulated type II collagen production by MPCs. These results suggest that SSBs are an effective platform for the generation of MPC-derived EVs with the potential to induce cartilage repair.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".