Combined delivery of chondroitinase ABC and human induced pluripotent stem cell-derived neuroepithelial cells promote tissue repair in an animal model of spinal cord injury
Bibliographic record
Abstract
The lack of tissue regeneration after traumatic spinal cord injury in animal models is largely attributed to the local inhibitory microenvironment. To overcome this inhibitory environment while promoting tissue regeneration, we investigated the combined delivery of chondroitinase ABC (chABC) with human induced pluripotent stem cell-derived neuroepithelial stem cells (NESCs). ChABC was delivered to the injured spinal cord at the site of injury by affinity release from a crosslinked methylcellulose (MC) hydrogel by injection into the intrathecal space. NESCs were distributed in a hydrogel comprised of hyaluronan and MC and injected into the spinal cord tissue both rostral and caudal to the site of injury. Cell transplantation led to reduced cavity formation, but did not improve motor function. While few surviving cells were found 2 weeks post injury, the majority of live cells were neurons, with only few astrocytes, oligodendrocytes, and progenitor cells. At 9 weeks post injury, there were more progenitor cells and a more even distribution of cell types compared to those at 2 weeks post injury, suggesting preferential survival and differentiation. Interestingly, animals that received cells and chABC had more neurons than animals that received cells alone, suggesting that chABC influenced the injury environment such that neuronal differentiation or survival was favoured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".