Bioengineering Strategies to Control Neural Stem/Progenitor Cell Differentiation
Bibliographic record
Abstract
Traumatic spinal cord injury is devastating, leading to lifelong paralysis with little hope of recovery based on current treatments. Neural stem/progenitor cells (NSPCs) offer the potential to promote regeneration after injury, providing a permissive environment to host cells. We have been particularly interested in sub‐acute injury models where both NSPC survival and regenerative potential are greatest. In our preliminary studies, we compared stem cells derived from the brain vs. those from the spinal cord in a transection injury model, where the stem cells were delivered in biocompatible hydrogel tubes of chitosan. Here we observed greater brain‐derived cell numbers and larger tissue bridges than spinal cord‐derived cells. In on‐going studies, we have refined our tube design to include microspheres for sustained, localized release of relevant factors. We have screened a number of factors in vitro to yield an enriched population of one cell type, either neuronal or glial. These factors are being explored for incorporation into the tubes and their impact measured on stem cell survival and differentiation in vitro and in an in vivo rat transection injury model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".