Biomaterials for neural-tissue engineering — Chitosan supports the survival, migration, and differentiation of adult-derived neural stem and progenitor cells
Bibliographic record
Abstract
Neural precursor cells (NPCs or stem and progenitor cells) are promising in transplantation strategies to treat an injury to the central nervous system, such as a spinal cord injury (SCI), because of their ability to differentiate into neurons and glia. Transplantation studies to date have met with limited success for a number of reasons, including poor cell survival. One way to encourage cell survival in injured tissue is to provide the cells with a scaffold to enhance their survival, their integration, and potentially their differentiation into appropriate cell types. Towards this end, four amine-functionalized hydrogels were screened in vitro for adult murine NPC viability, migration, and differentiation: chitosan, poly(oligoethylene oxide dimethacrylate-co-2-amino ethyl methacrylate), blends of poly(oligoethylene oxide dimethacrylate-co-2-amino ethyl methacrylate), and poly(vinyl alcohol), and poly(glycerol dimethacrylate-co-2-amino ethyl methacrylate). The greatest cell viability was found on chitosan at all times examined, Chitosan had the greatest surface amine content and the lowest equilibrium water content, which likely contributed to the greater NPC viability observed over three weeks in culture. Only chitosan supported survival of multipotent stem cells and the differentiation of the progenitors into neurons, astrocytes, and oligodendrocytes. Plating intact NPC colonies revealed greater cell migration on chitosan relative to the other hydrogels. Importantly, long term cultures on chitosan showed no significant difference in total cell counts over time, suggesting no net cell growth. Together, these findings reveal chitosan as a promising material for the delivery of adult NPC cell-based therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".