<i>In Vitro</i> Maturation of Human iPSC-Derived Neuroepithelial Cells Influences Transplant Survival in the Stroke-Injured Rat Brain
Bibliographic record
Abstract
Stem cell transplantation is a promising strategy for brain tissue regeneration; yet, despite some success, cell survival following transplantation remains low. In this study, we demonstrate that cell viability is enhanced by control over maturation of neuronal precursor cells, which are delivered in an injectable blend of hyaluronan and methylcellulose. We selected three subpopulations of human neuronal precursor cells derived from a cortically specified neuroepithelial stem cell (cNESC) population based on differences in expression of multipotent and neuron-specific proteins: early-, mid-, and late-differentiated neurons. These cells were transplanted into an endothelin-1 stroke-injured rat brain and their survival and fate were investigated 1 week later. Significantly, more cells were found in the brain after transplanting early- or mid- differentiated cNESCs compared to the late-differentiated population. The mid-differentiated population also had significantly more β-III tubulin-positive cells than either the early- or late-differentiated populations. These results suggest that maturity has a significant impact on cell survival following transplantation and cells with an intermediate maturity differentiate to neurons.
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) | 0.000 | 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) | 0.001 | 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".