Combination of Multifaceted Strategies to Maximize the Therapeutic Benefits of Neural Stem Cell Transplantation for Spinal Cord Repair
Bibliographic record
Abstract
Neural stem cells (NSCs) possess therapeutic potentials to reverse complex pathological processes following spinal cord injury (SCI), but many obstacles remain that could not be fully overcome by NSC transplantation alone. Combining complementary strategies might be required to advance NSC-based treatments to the clinical stage. The present study was undertaken to examine whether combination of NSCs, polymer scaffolds, neurotrophin-3 (NT3), and chondroitinase, which cleaves chondroitin sulfate proteoglycans at the interface between spinal cord and implanted scaffold, could provide additive therapeutic benefits. In a rat hemisection model, poly(ɛ-caprolactone) (PCL) was used as a bridging scaffold and as a vehicle for NSC delivery. The PCL scaffolds seeded with F3 NSCs or NT3 overexpressing F3 cells (F3.NT3) were implanted into hemisected cavities. F3.NT3 showed better survival and migration, and more frequently differentiated into neurons and oligodendrocytes than F3 cells. Animals with PCL scaffold containing F3.NT3 cells showed the best locomotor recovery, and motor evoked potentials (MEPs) following transcranial magnetic stimulation were recorded only in PCL-F3.NT3 group in contralateral, but not ipsilateral, hindlimbs. Implantation of PCL scaffold with F3.NT3 cells increased NT3 levels, promoted neuroplasticity, and enhanced remyelination of contralateral white matter. Combining chondroitinase treatment after PCL-F3.NT3 implantation further enhanced cell migration and promoted axonal remodeling, and this was accompanied by augmented locomotor recovery and restoration of MEPs in ipsilateral hindlimbs. We demonstrate that combining multifaceted strategies can maximize the therapeutic benefits of NSC transplantation for SCI. Our results may have important clinical implications for the design of future NSC-based strategies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".