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Record W2973993435 · doi:10.1242/bio.044867

Failure to identify modifiers of <i>NEBULIN</i> related nemaline myopathy in two pre-clinical models of the disease

2019· article· en· W2973993435 on OpenAlex
Boyang Qiu, Julie Ruston, Henk Granzier, Monica J. Justice, James J. Dowling

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiology Open · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesHospital for Sick Children
KeywordsNebulinNemaline myopathyBiologySarcomereZebrafishMyopathyMutationExonGeneticsDiseaseBioinformaticsGenePathologyMedicineCell biologyTitinMyocyte

Abstract

fetched live from OpenAlex

Nemaline myopathy is a rare neuromuscular disorder that affects 1 in 50,000 live births, with prevalence as high as 1 in 20,000 in certain populations. 13 genes have been linked to nemaline myopathy, all of which are associated with the thin filament of the muscle sarcomere. Of the 13 associated genes, mutations in NEBULIN accounts for up to 50% of all cases. Currently, the disease is incompletely understood and there are no available therapeutics for patients. To address this urgent need for effective treatments for patients affected by NM, we conducted a large scale chemical screen in a zebrafish model of NEB related nemaline myopathy and a ENU based genetic screen in a mouse model of NEB exon 55 deletion, the most common NEBULIN mutation in nemaline myopathy patients. Neither screen was able to identify a candidate for therapy development, highlighting the need to transition from conventional chemical therapeutics to gene-based therapies for the treatment of nemaline myopathy.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.046
GPT teacher head0.417
Teacher spread0.372 · 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