Transplantation of human embryonic stem cell‐derived neural precursor cells and enriched environment after cortical stroke in rats: cell survival and functional recovery
Bibliographic record
Abstract
Cortical stem cell transplantation may help replace lost brain cells after stroke and improve the functional outcome. In this study, we transplanted human embryonic stem cell (hESC)-derived neural precursor cells (hNPCs) or vehicle into the cortex of rats after permanent distal middle cerebral artery occlusion (dMCAO) or sham-operation, and followed functional recovery in the cylinder and staircase tests. The hNPCs were examined prior to transplantation, and they expressed neuroectodermal markers but not markers for undifferentiated hESCs or non-neural cells. The rats were housed in either enriched environment or standard cages to examine the effects of additive rehabilitative therapy. In the behavioral tests dMCAO groups showed significant impairments compared with sham group before transplantation. Vehicle groups remained significantly impaired in the cylinder test 1 and 2 months after vehicle injection, whereas hNPC transplanted groups did not differ from the sham group. Rehabilitation or hNPC transplantation had no effect on reaching ability measured in the staircase test, and no differences were found in the cortical infarct volumes. After 2 months we measured cell survival and differentiation in vivo using stereology and confocal microscopy. Housing had no effect on cell survival or differentiation. The majority of the transplanted hNPCs were positive for the neural precursor marker nestin. A portion of transplanted cells expressed neuronal markers 2 months after transplantation, whereas only a few cells co-localized with astroglial or oligodendrocyte markers. In conclusion, hESC-derived neural precursor transplants provided some improvement in sensorimotor function after dMCAO, but did not restore more complicated sensorimotor functions.
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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) | 0.000 | 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 | 0.001 |
| 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".