Transplantation of Induced Neural Stem Cells Derived from Human Fibroblasts Promotes Functional Recovery of Spinal Cord Injury in Rats
Bibliographic record
Abstract
Background: Traumatic injury to the spinal cord causes cell death, demyelination, axonal degeneration, and cavitation, resulting in functional motor and sensory loss. Stem cell therapy as a promising approach for spinal cord injury (SCI) has some challenges, such as immunological responses to grafted cells. Transplantation of autologous trans-differentiated cells can be a useful strategy to overcome this problem. Materials and Methods: In this research, we transplanted human-induced neural stem cells (hiNSCs), which were trans-differentiated from adult human fibroblasts into the injured spinal cord of adult rats on day seven post-injury. Before transplantation, hiNSCs were explored for expressing NSCs general protein and genes and also, their normal karyotype was examined. After hiNSCs transplantation, behavioural tests (BBB score and grid walk tests) were performed weekly and finally histological assessment was done for exploring the cell fates and migration. Results: Our results showed the cell viability, differentiation, and migration of transplanted hiNSCs was significantly improved in the injured site of the spinal cord up to seven weeks after the SCI. Also, the behavioural analysis revealed the enhanced locomotor functions of the animals that underwent transplantation after seven weeks. Our data provide strong evidence in support of the feasibility of hiNSCs for cell-based therapy in SCI rats.
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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) | 0.002 | 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".