Use of Brain-Derived Stem/Progenitor Cells and Derived Extracellular Vesicles to Repair Damaged Neural Tissues: Lessons Learned from Connective Tissue Repair Regarding Variables Limiting Progress and Approaches to Overcome Limitations
Bibliographic record
Abstract
Pluripotent neural stem or progenitor cells (NSC/NPC) have been reported in the brains of adult preclinical models for decades, as have mesenchymal stem/stromal cells (MSC) been reported in a variety of tissues from adults. Based on their in vitro capabilities, these cell types have been used extensively in attempts to repair/regenerate brain and connective tissues, respectively. In addition, MSC have also been used in attempts to repair compromised brain centres. However, success in treating chronic neural degenerative conditions such as Alzheimer's disease, Parkinson's disease, and others with NSC/NPC has been limited, as have the use of MSC in the treatment of chronic osteoarthritis, a condition affecting millions of individuals. However, connective tissues are likely less complex than neural tissues regarding cell organization and regulatory integration, but some insights have been gleaned from the studies regarding connective tissue healing with MSC that may inform studies attempting to initiate repair and regeneration of neural tissues compromised acutely or chronically by trauma or disease. This review will discuss the similarities and differences in the applications of NSC/NPC and MSC, where some lessons have been learned, and potential approaches that could be used going forward to enhance progress in the application of cellular therapy to facilitate repair and regeneration of complex structures in the brain. In particular, variables that may need to be controlled to enhance success are discussed, as are different approaches such as the use of extracellular vesicles from stem/progenitor cells that could be used to stimulate endogenous cells to repair the tissues rather than consider cell replacement as the primary option. Caveats to all these efforts relate to whether cellular repair initiatives will have long-term success if the initiators for neural diseases are not controlled, and whether such cellular initiatives will have long-term success in a subset of patients if the neural diseases are heterogeneous and have multiple etiologies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".