Electric field stimulation boosts neuronal differentiation of neural stem cells for spinal cord injury treatment via PI3K/Akt/GSK-3β/β-catenin activation
Bibliographic record
Abstract
BACKGROUND: Neural stem cells (NSCs) are considered as candidates for cell replacement therapy in many neurological disorders. However, the propensity for their differentiation to proceed more glial rather than neuronal phenotypes in pathological conditions limits positive outcomes of reparative transplantation. Exogenous physical stimulation to favor the neuronal differentiation of NSCs without extra chemical side effect could alleviate the problem, providing a safe and highly efficient cell therapy to accelerate neurological recovery following neuronal injuries. RESULTS: With 7-day physiological electric field (EF) stimulation at 100 mV/mm, we recorded the boosted neuronal differentiation of NSCs, comparing to the non-EF treated cells with 2.3-fold higher MAP2 positive cell ratio, 1.6-fold longer neuronal process and 2.4-fold higher cells ratio with neuronal spontaneous action potential. While with the classical medium induction, the neuronal spontaneous potential may only achieve after 21-day induction. Deficiency of either PI3Kγ or β-catenin abolished the above improvement, demonstrating the requirement of the PI3K/Akt/GSK-3β/β-catenin cascade activation in the physiological EF stimulation boosted neuronal differentiation of NSCs. When transplanted into the spinal cord injury (SCI) modelled mice, these EF pre-stimulated NSCs were recorded to develop twofold higher proportion of neurons, comparing to the non-EF treated NSCs. Along with the boosted neuronal differentiation following transplantation, we also recorded the improved neurogenesis in the impacted spinal cord and the significantly benefitted hind limp motor function repair of the SCI mice. CONCLUSIONS: In conclusion, we demonstrated physiological EF stimulation as an efficient method to boost the neuronal differentiation of NSCs via the PI3K/Akt/GSK-3β/β-catenin activation. Pre-treatment with the EF stimulation induction before NSCs transplantation would notably improve the therapeutic outcome for neurogenesis and neurofunction recovery of 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".