Exploring the Application of Electrical Stimulation to Enhance Resident Neural Precursor Activation and Viral Transduction Efficacy: A Combined Approach to Promote Neural Repair
Bibliographic record
Abstract
Electrical stimulation to activate neural stem and progenitor cells (neural precursor cells) has been examined both in vitro and in vivo. Recent work has established optimal parameters for cortical stimulation that leads to an expansion of the neural stem cell pool and cathodal migration, as well as enhanced neural differentiation. In this work, we aimed to examine striatal stimulation parameters as a means to broaden the approach of electrical stimulation for NPC activation. We hypothesized that sufficient NPC activation would be observed with a cathodal pulse delivered to the striatum, such that NPC niche, lining the lateral ventricle, perceive a similar electric field as was used with cortical stimulation. We used COMSOL Multiphysics modeling and demonstrated that a cathodal current pulse amplitude of -200 µA leads to an expansion of the neural stem cell pool; however, migration towards the striatum was not observed. With the goal of augmenting regenerative strategies in the injured brain, we asked whether electrical stimulation could enhance therapies using viral mediated gene delivery. We performed in vitro studies that combined electrical stimulation and adeno-associated viral delivery, determining that the application of 250 mV/mm of electrical field significantly enhanced viral transduction of differentiated neural stem cell progeny, providing insight into potential combinatory strategies for neural repair.
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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".