MétaCan
Menu
Back to cohort
Record W1858890378 · doi:10.1002/jcp.25230

Ex Vivo Assay of Electrical Stimulation to Rat Sciatic Nerves: Cell Behaviors and Growth Factor Expression

2015· article· en· W1858890378 on OpenAlexafffund
Zhiyong Du, O. O. Bondarenko, Dingkun Wang, Mahmoud Rouabhia, Ze Zhang

Bibliographic record

VenueJournal of Cellular Physiology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchChina Scholarship Council
KeywordsNeuriteNeurotrophic factorsStimulationSchwann cellAxonCell biologyWallerian degenerationBiologySciatic nerveEx vivoNeuroscienceCell cultureNeurotrophinIn vivoIn vitroAnatomyBiochemistryReceptor

Abstract

fetched live from OpenAlex

Neurite outgrowth and axon regeneration are known to benefit from electrical stimulation. However, how neuritis and their surroundings react to electrical field is difficult to replicate by monolayer cell culture. In this work freshly harvested rat sciatic nerves were cultured and exposed to two types of electrical field, after which time the nerve tissues were immunohistologically stained and the expression of neurotrophic factors and cytokines were evaluated. ELISA assay was used to confirm the production of specific proteins. All cell populations survived the 48 h culture with little necrosis. Electrical stimulation was found to accelerate Wallerian degeneration and help Schwann cells to switch into migratory phenotype. Inductive electrical stimulation was shown to upregulate the secretion of multiple neurotrophic factors. Cellular distribution in nerve tissue was altered upon the application of an electrical field. This work thus presents an ex vivo model to study denervated axon in well controlled electrical field, bridging monolayer cell culture and animal experiment. It also demonstrated the critical role of electrical field distribution in regulating cellular activities.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.278
Teacher spread0.246 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cellular PhysiologySame topicNerve injury and regenerationFrench-language works237,207