Synergistic Effects of Transplanted Adult Neural Stem/Progenitor Cells, Chondroitinase, and Growth Factors Promote Functional Repair and Plasticity of the Chronically Injured Spinal Cord
Bibliographic record
Abstract
The transplantation of neural stem/progenitor cells (NPCs) is a promising therapeutic strategy for spinal cord injury (SCI). However, to date NPC transplantation has exhibited only limited success in the treatment of chronic SCI. Here, we show that chondroitin sulfate proteoglycans (CSPGs) in the glial scar around the site of chronic SCI negatively influence the long-term survival and integration of transplanted NPCs and their therapeutic potential for promoting functional repair and plasticity. We targeted CSPGs in the chronically injured spinal cord by sustained infusion of chondroitinase ABC (ChABC). One week later, the same rats were treated with transplants of NPCs and transient infusion of growth factors, EGF, bFGF, and PDGF-AA. We demonstrate that perturbing CSPGs dramatically optimizes NPC transplantation in chronic SCI. Engrafted NPCs successfully integrate and extensively migrate within the host spinal cord and principally differentiate into oligodendrocytes. Furthermore, this combined strategy promoted the axonal integrity and plasticity of the corticospinal tract and enhanced the plasticity of descending serotonergic pathways. These neuroanatomical changes were also associated with significantly improved neurobehavioral recovery after chronic SCI. Importantly, this strategy did not enhance the aberrant synaptic connectivity of pain afferents, nor did it exacerbate posttraumatic neuropathic pain. For the first time, we demonstrate key biological and functional benefits for the combined use of ChABC, growth factors, and NPCs to repair the chronically injured spinal cord. These findings could potentially bring us closer to the application of NPCs for patients suffering from chronic SCI or other conditions characterized by the formation of a glial scar.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".