The role of mesenchymal stem cells in peripheral nerve regeneration
Bibliographic record
Abstract
Taking into account the imperfect results of treatment of peripheral nerve injuries and despite the large number of already performed clinical and experimental studies on the regeneration of peripheral nerves, the search for new effective methods of treatment of this pathology remains relevant until now. Cell therapy is one of the innovative and promising therapeutic approaches in the field of nerve regeneration. Mesenchymal stem cells (MSCs) were first introduced in 1966 by Friedenstein and colleagues. This is a class of multipotent stem cells of mesodermal origin, which have low immunogenicity, but high capacity for self-renewal and multidirectional differentiation potential. They were found in almost all tissues. According to the International Society for Cell Therapy (ISCT, Vancouver, Canada), human MSCs express CD73, CD105, CD90 and lack hematopoietic stem cell markers (CD34, CD14, CD45, CD11b, CD79a, and HLA-DR). There is no consensus regarding the main mechanism of action of MSCs in stimulating nerve regeneration, but in general, most likely, they perform a trophic function, inhibit proapoptotic pathways and enhance the intrinsic regenerative capacity of the nerve. The ability of MSCs to produce various immunoregulatory factors is also known, which leads to the reduction of an inadequate inflammatory response after nerve damage. MSCs can transdifferentiate into Schwann cells, acquire a Schwann-like phenotype, or stimulate the activity of endogenous Schwann cells, which are one of the key regulators of reparative nerve regeneration. MSCs also take part in the remyelination of damaged nerves due to the synthesis and release of myelin proteins MBP or P0. MSCs create an enhanced neuroprotective microenvironment that prevents nerve degeneration, supports axon growth, neurogenesis, and cellular metabolism. The property of stem cells to produce neurotrophic factors is also well known. It is interesting to note that endogenous stem cells, including those circulating in peripheral blood, are much less studied. A detailed study of various pharmacological, gene-modifying, or biomodifying methods of endogenous stem cell mobilization, their properties, and identification may be promising in regenerative medicine in general and peripheral nerve regeneration in particular.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".