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Record W4312138116 · doi:10.3389/fnmol.2022.1054573

Modeling neuromuscular diseases in zebrafish

2022· review· en· W4312138116 on OpenAlexafffund
Jaskaran Singh, Shunmoogum A. Patten

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2022
Typereview
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalInstitut National de la Recherche Scientifique
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsZebrafishMechanism (biology)Exome sequencingBiologyDrug developmentExomeGenetic heterogeneityBioinformaticsNeuroscienceComputational biologyMedicineGeneticsMutationPhenotypeDrugGenePharmacology

Abstract

fetched live from OpenAlex

Neuromuscular diseases are a diverse group of conditions that affect the motor system and present some overlapping as well as distinct clinical manifestations. Although individually rare, the combined prevalence of NMDs is similar to Parkinson's. Over the past decade, new genetic mutations have been discovered through whole exome/genome sequencing, but the pathogenesis of most NMDs remains largely unexplored. Little information on the molecular mechanism governing the progression and development of NMDs accounts for the continual failure of therapies in clinical trials. Different aspects of the diseases are typically investigated using different models from cells to animals. Zebrafish emerges as an excellent model for studying genetics and pathogenesis and for developing therapeutic interventions for most NMDs. In this review, we describe the generation of different zebrafish genetic models mimicking NMDs and how they are used for drug discovery and therapy development.

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)0.001
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 score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.047
GPT teacher head0.339
Teacher spread0.292 · 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

Citations19
Published2022
Admission routes2
Has abstractyes

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