Emerging Safety of Intramedullary Transplantation of Human Neural Stem Cells in Chronic Cervical and Thoracic Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Human central nervous system stem cells (HuCNS-SC) are multipotent adult stem cells with successful engraftment, migration, and region-appropriate differentiation after spinal cord injury (SCI). OBJECTIVE: To present data on the surgical safety profile and feasibility of multiple intramedullary perilesional injections of HuCNS-SC after SCI. METHODS: Intramedullary free-hand (manual) transplantation of HuCNS-SC cells was performed in subjects with thoracic (n = 12) and cervical (n = 17) complete and sensory incomplete chronic traumatic SCI. RESULTS: Intramedullary stem cell transplantation needle times in the thoracic cohort (20 M HuCNS-SC) were 19:30 min and total injection time was 42:15 min. The cervical cohort I (n = 6), demonstrated that escalating doses of HuCNS-SC up to 40 M range were well tolerated. In cohort II (40 M, n = 11), the intramedullary stem cell transplantation needle times and total injection time was 26:05 ± 1:08 and 58:14 ± 4:06 min, respectively. In the first year after injection, there were 4 serious adverse events in 4 of the 12 thoracic subjects and 15 serious adverse events in 9 of the 17 cervical patients. No safety concerns were considered related to the cells or the manual intramedullary injection. Cervical magnetic resonance images demonstrated mild increased T2 signal change in 8 of 17 transplanted subjects without motor decrements or emerging neuropathic pain. All T2 signal change resolved by 6 to 12 mo post-transplant. CONCLUSION: A total cell dose of 20 M cells via 4 and up to 40 M cells via 8 perilesional intramedullary injections after thoracic and cervical SCI respectively proved safe and feasible using a manual injection technique.
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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 | 0.001 |
| 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 | 0.001 |
| 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".