Stem Cell-Based Cell Therapy for Spinal Cord Injury
Bibliographic record
Abstract
Traumatic injuries to the spinal cord lead to severe and permanent neurological deficits. Although no effective therapeutic option is currently available, recent animal studies have shown that cellular transplantation strategies hold promise to enhance functional recovery after spinal cord injury (SCI). This review is to analyze the experiments where transplantation of stem/progenitor cells produced successful functional outcome in animal models of SCI. There is no consensus yet on what kind of stem/progenitor cells is an ideal source for cellular grafts. Three kinds of stem/progenitor cells have been utilized in cell therapy in animal models of SCI: embryonic stem cells, bone marrow mesenchymal stem cells, and neural stem cells. Neural stem cells or fate-restricted neuronal or glial progenitor cells were preferably used because they have clear capacity to become neurons or glial cells after transplantation into the injured spinal cord. At least a part of functional deficits after SCI is attributable to chronic progressive demyelination. Therefore, several studies transplanted glial-restricted progenitors or oligodendrocyte precursors to target the demyelination process. Directed differentiation of stem/progenitor cells to oligodendrocyte lineage prior to transplantation or modulation of microenvironment in the injured spinal cord to promote oligodendroglial differentiation seems to be an effective strategy to increase the extent of remyelination. Transplanted stem/progenitor cells can also contribute to promoting axonal regeneration by functioning as cellular scaffolds for growing axons. Combinatorial approaches using polymer scaffolds to fill the lesion cavity or introducing regeneration-promoting genes will greatly increase the efficacy of cellular transplantation strategies for SCI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".