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A novel ex vivo assay to define charge-balanced electrical stimulation parameters for neural precursor cell activation in vivo

2023· article· en· W4318263111 on OpenAlexafffund
Kylie S.K. Lau, Tianhao Chen, Stephanie N. Iwasa, Matthew Volpatti, Miloš R. Popović, Cindi M. Morshead

Bibliographic record

VenueBrain Research · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsEx vivoIn vivoStimulationNeuroscienceNeural stem cellEndogenyBiologyCell biologyStem cellBiochemistry

Abstract

fetched live from OpenAlex

Endogenous neural stem cells and their progeny (together termed neural precursor cells (NPCs)) are promising candidates to facilitate neuroregeneration. Charge-balanced biphasic monopolar stimulation (BPMP) is a clinically relevant approach that can activate NPCs both in vitro and in vivo. Herein, we established a novel ex vivo stimulation system to optimize the efficacy of BPMP electric field (EF) application in activating endogenous NPCs. Using the ex vivo system, we discerned that cathodal amplitude of 200 μA resulted in the greatest NPC pool expansion and enhanced cathodal migration. Application of the same stimulation parameters in vivo resulted in the same NPC activation in the mouse brain. The design and implementation of the novel ex vivo model bridges the gap between in vitro and in vivo systems, enabling a moderate throughput stimulation system to explore and optimize EF parameters that can be applied to clinically relevant brain injury/disease models.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.158
GPT teacher head0.389
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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