Abstracts from The 35 <sup>th</sup> Annual National Neurotrauma Symposium July 7–12, 2017 Snowbird, Utah
Bibliographic record
Abstract
Human induced pluripotent stem cell-derived neural stem cells (hiPS-NSCs) represent an exciting therapeutic strategy for traumatic spinal cord injury (SCI) as they can replace lost neural circuits, remyelinate denuded axons and provide local trophic support.Unfortunately, most patients are in the chronic phase of their injuries where dense chondroitin sulfate proteoglycan (CSPG) scarring significantly impairs neurite outgrowth and regenerative cell migration.Several scarmodifying enzymes have been shown to synergistically enhance NSCmediated recovery, however, nonspecific intrathecal administration can produce off-target effects.We aimed to generate a geneticallyengineered line of hiPS-NSCs, termed Spinal Microenvironment Modifying and Regenerative Therapeutic (SMaRT) cells, uniquely capable of expressing a scar-modifying enzyme within the host environment to enhance functional recovery.A proprietary enzyme was non-virally integrated into hiPS-NSCs and a monocloncal line of SMaRT cells was generated and extensively characterized.The expressed enzyme rapidly degrades CSPGs on biochemical assays and allows neurons to extend into CSPG-rich regions in vitro.Furthermore, unlike wild-type hiPS-NSC media, conditioned SMaRT cell media can degrade post-injury rodent CSPGs in ex vivo injured cord cryosections.T-cell deficient rats (N = 60) with translationallyrelevant chronic C6-7 clip-contusion injuries have been randomized to receive: (1) vehicle, (2) hiPS-NSCs, (3) SMaRT cells, or (4) sham surgery (laminectomy).While blinded sensorimotor behavioural assessments and rehabilitation are ongoing with a long-term 32-week endpoint, interim histologic analysis shows that grafted human cells are extending remarkably long ( ‡ 20,000 lm) axons along host white matter tracts in the rostral and caudal directions.This work provides exciting proof-of-concept data that genetically-engineered SMaRT cells can degrade CSPGs in vitro and that human NSC transplants can grow long axons in chronic cervical SCI to potentially form a bridge for sensorimotor signal transmission.This work is generously supported by the Canadian Institutes of Health Research, Phillip and Peggy DeZwirek, OIRM, and the Krembil Foundation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.499 | 0.239 |
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".