GP.04 Smart human neural stem cells to degrade scar and optimize regeneration after traumatic cervical spinal cord injury
Bibliographic record
Abstract
Background: Human induced pluripotent stem cell-derived neural stem cells (hiPS-NSCs) represent an exciting therapeutic approach for traumatically spinal cord injury (SCI). Unfortunately, most patients are the in chronic injury phase where a dense perilesional chondroitin sulfate proteoglycan (CSPG) scar significantly hinders regeneration. CSPG-degrading enzymes can enhance NSC-mediated recovery, however, nonspecific intrathecal administration causes off-target effects. We aimed to genetically engineer hiPS-NSCs to express a scar-degrading ENZYME into their local environment to enhance functional recovery. Methods: A bicistronic scar-degrading ENZYME and RFP reporter vector was non-virally integrated into hiPS-NSCs and monoclonalized. ENZYME activity was assessed by WST-1 and DMMB biochemical assays and an in vitro CSPG spot assay with hiPS-NSC-derived neurons. To assess in vivo efficacy, T-cell deficient rats (N=60) with chronic (8wk) C6-7 SCIs were randomized to receive (1)SMaRT cells, (2)hiPS-NSCs, (3)vehicle, or (4)sham surgery. Results: SMaRT cells retained key hiPS-NSC characteristics while stably expressing ENZYME. The expressed ENZYME could appropriately degrade in vitro and ex vivo CSPGs. While blinded neurobehavioural and immunohistochemical assessments are ongoing at 40wks post-injury, an interim analysis demonstrated human cells extending remarkably long (≥20,000µm) axons along host white matter tracts. Conclusions: This work provides exciting proof-of-concept data that genetically-engineered SMaRT cells can degrade CSPGs and human NSCs can extend long-distance processes in chronic 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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".