Extracellular Vesicles Generated by Mesenchymal Stem Cells in Stirred Suspension Bioreactors Promote Angiogenesis in Human-Brain-Derived Endothelial Cells
Bibliographic record
Abstract
Interrupted blood flow in the brain due to ischemic injuries such as ischemic stroke or traumatic brain injury results in irreversible brain damage, leading to cognitive impairment associated with inflammation, disruption of the blood-brain barrier (BBB), and cell death. Since the BBB only allows entry to a small class of drugs, many drugs used to treat ischemia in other tissues have failed in brain-related disorders. The administration of mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) has shown promise in improving the functional recovery of the brain following cerebral ischemia by inducing blood vessel formation. To facilitate such a treatment approach, it is necessary to develop bioprocesses that can produce therapeutically relevant MSC-EVs in a reproducible and scalable manner. This study evaluated the feasibility of using stirred suspension bioreactors (SSBs) to scale-up the serum-free production of pro-angiogenic MSC-EVs under clinically relevant physioxic conditions. It was found that MSCs grown in SSBs generated EVs that stimulated angiogenesis in cerebral microvascular endothelial cells, supporting the use of SSBs to produce MSC-EVs for application in cerebral ischemia. These properties were impaired at higher cell confluency, outlining the importance of considering the time of harvest when developing bioprocesses to manufacture EV populations.
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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) | 0.001 | 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".