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Record W4377091470 · doi:10.3233/jnd-221646

Presynaptic Congenital Myasthenic Syndromes: Understanding Clinical Phenotypes through <i>In vivo</i> Models

2023· review· en· W4377091470 on OpenAlexafffund
Alessia Pugliese, Stephen H. Holland, Carmelo Rodolico, Hanns Lochmüller, Sally Spendiff

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2023
Typereview
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversity of OttawaOttawa HospitalChildren's Hospital of Eastern Ontario
FundersCanadian Institutes of Health ResearchCanada Research ChairsMuscular Dystrophy Canada
KeywordsNeuromuscular junctionPhenotypeCongenital myasthenic syndromeNeuroscienceSynaptic vesicleMedicineSynapseDiseaseWeaknessIn vivoBiologyBioinformaticsGeneMutationGeneticsInternal medicineAnatomyVesicle

Abstract

fetched live from OpenAlex

Presynaptic congenital myasthenic syndromes (CMS) are a group of genetic disorders affecting the presynaptic side of the neuromuscular junctions (NMJ). They can result from a dysfunction in acetylcholine (ACh) synthesis or recycling, in its packaging into synaptic vesicles, or its subsequent release into the synaptic cleft. Other proteins involved in presynaptic endplate development and maintenance can also be impaired.Presynaptic CMS usually presents during the prenatal or neonatal period, with a severe phenotype including congenital arthrogryposis, developmental delay, and apnoeic crisis. However, milder phenotypes with proximal muscle weakness and good response to treatment have been described. Finally, many presynaptic genes are expressed in the brain, justifying the presence of additional central nervous system symptoms.Several animal models have been developed to study CMS, providing the opportunity to identify disease mechanisms and test treatment options. In this review, we describe presynaptic CMS phenotypes with a focus on in vivo models, to better understand CMS pathophysiology and define new causative genes.

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)0.001
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 score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.296
GPT teacher head0.415
Teacher spread0.119 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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