Charge-Balanced Electrical Stimulation Can Modulate Neural Precursor Cell Migration in the Presence of Endogenous Electric Fields in Mouse Brains
Bibliographic record
Abstract
Abstract Electric fields (EFs) can direct cell migration and are crucial during development and tissue repair. We previously reported neural precursor cells (NPCs) are electrosensitive cells that can undergo rapid and directed migration towards the cathode using charge-balanced electrical stimulation in vitro . Here, we investigate the ability of electrical stimulation to direct neural precursor migration in mouse brains in vivo . To visualize migration, fluorescent adult murine neural precursors were transplanted onto the corpus callosum of adult male mice and intracortical platinum wire electrodes were implanted medial (cathode) and lateral (anode) to the injection site. We applied a charge-balanced biphasic monopolar stimulation waveform for three sessions per day, for 3 or 6 d. Irrespective of stimulation, the transplanted neural precursors had a propensity to migrate laterally along the corpus callosum, and applied stimulation affected that migration. Further investigation revealed an endogenous EF along the corpus callosum that correlated with the lateral migration, suggesting that the applied EF would need to overcome endogenous cues. There was no difference in transplanted cell differentiation and proliferation, or inflammatory cell numbers near the electrode leads and injection site comparing stimulated and implanted non-stimulated brains. Our results support that endogenous and applied EFs are important considerations for designing cell therapies for tissue repair in vivo.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".