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Record W3086617206 · doi:10.3389/fcell.2020.573303

Age Bias in Zebrafish Models of Epilepsy: What Can We Learn From Old Fish?

2020· review· en· W3086617206 on OpenAlexafffund
Sung‐Joon Cho, Eugene Park, Andrew Baker, Aylin Y. Reid

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsSt. Michael's HospitalUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Research FoundationNational Research Foundation of KoreaSavoy FoundationToronto General and Western Hospital Foundation
KeywordsZebrafishEpilepsyNeurogenesisNeuroscienceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Zebrafish are a powerful tool for investigating epilepsy. Mammalian seizures can be recapitulated molecularly, behaviorally, and electrophysiologically, using a fraction of the resources required for experiments in mammals. Larval zebrafish offer exceptionally economical and high-throughput approaches and are amenable to state-of-the-art genetic engineering techniques, providing valuable transgenic models of human diseases. For these reasons, larvae tend to be chosen for studying epilepsy, but the value of adult zebrafish may be underappreciated. Zebrafish exhibit transient larval - adult duality. The incompletely developed neural system of larval zebrafish may limit the translation of complex neurological disorders. Larval zebrafish go through dynamic changes during ontogenesis, whereas adult zebrafish are physiologically more stable. Adult zebrafish have a full range of complex brain structures and functions, such as an endothelial blood-brain barrier and adult neurogenesis, both are significant factors in epilepsy research. This review highlights the differences between larval and adult zebrafish that should be considered in pathophysiological and pharmacological studies of epilepsy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.039
GPT teacher head0.290
Teacher spread0.251 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations27
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Cell and Developmental BiologySame topicZebrafish Biomedical Research ApplicationsFrench-language works237,207