Overexpression of Bcl‐X<sub>L</sub> in human neural stem cells promotes graft survival and functional recovery following transplantation in spinal cord injury
Bibliographic record
Abstract
Transplantation of neural stem cells (NSCs) has shown promise for improving functional recovery after spinal cord injury (SCI). The inhospitable milieu of injured spinal cord, however, does not support survival of grafted NSCs, reducing therapeutic efficacy of transplantation. The present study sought to examine whether overexpression of antiapoptotic gene Bcl-X(L) in NSCs could promote graft survival and functional recovery following transplantation in rat contusive SCI model. A human NSC line (HB1.F3) was transduced with a retroviral vector encoding Bcl-X(L) to generate Bcl-X(L)-overexpressing NSCs (HB1.F3.Bcl-X(L)). Overexpression of Bcl-X(L) conferred resistance to staurosporine-mediated apoptosis. The number of HB1.F3.Bcl-X(L) cells was 1.5-fold higher at 2 weeks and 10-fold higher at 7 weeks posttransplantation than that of HB1.F3 cells. There was no decline in the number of HB1.F3.Bcl-X(L) cells between 2 and 7 weeks, indicating that Bcl-X(L) overexpression completely blocked cell death occurring between these two time points. Transplantation of HB1.F3.Bcl-X(L) cells improved locomotor scores and enhanced accuracy of hindlimb placement in a grid walk. Approximately 10% of surviving NSCs differentiated into oligodendrocytes. Surviving NSCs produced brain-derived neurotrophic factor (BDNF), and the level of BDNF was significantly increased only in the HB1.F3.Bcl-X(L) group. Transplantation of HB1.F3.Bcl-X(L) cells reduced cavity volumes and enhanced white matter sparing. Finally, HB1.F3.Bcl-X(L) grafts enhanced connectivity between the red nucleus and the spinal cord below the lesion. These results suggest that enhancing graft survival with antiapoptotic gene can potentiate therapeutic benefits of NSC-based therapy for SCI.
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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 | 0.000 |
| 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".