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Record W4392016091 · doi:10.3390/biom14030256

The Dysferlinopathies Conundrum: Clinical Spectra, Disease Mechanism and Genetic Approaches for Treatments

2024· article· en· W4392016091 on OpenAlexafffund
Saeed Anwar, Toshifumi Yokota

Bibliographic record

VenueBiomolecules · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Alberta
FundersAustralasian Gynaecological Endoscopy and Surgery SocietyCanadian Institutes of Health ResearchAlberta InnovatesCanada Foundation for InnovationWomen and Children's Health Research InstituteMinistry of Advanced Education, Government of AlbertaBC Children’s Hospital FoundationAlberta Innovates - Health SolutionsRare Disease Foundation
KeywordsDiseaseMechanism (biology)Clinical trialPhenotypeBioinformaticsGenetic heterogeneityMedicineComputational biologyClinical phenotypeWeaknessDysferlinBiologyGeneticsGenePathology

Abstract

fetched live from OpenAlex

Dysferlinopathies refer to a spectrum of muscular dystrophies that cause progressive muscle weakness and degeneration. They are caused by mutations in the DYSF gene, which encodes the dysferlin protein that is crucial for repairing muscle membranes. This review delves into the clinical spectra of dysferlinopathies, their molecular mechanisms, and the spectrum of emerging therapeutic strategies. We examine the phenotypic heterogeneity of dysferlinopathies, highlighting the incomplete understanding of genotype-phenotype correlations and discussing the implications of various DYSF mutations. In addition, we explore the potential of symptomatic, pharmacological, molecular, and genetic therapies in mitigating the disease’s progression. We also consider the roles of diet and metabolism in managing dysferlinopathies, as well as the impact of clinical trials on treatment paradigms. Furthermore, we examine the utility of animal models in elucidating disease mechanisms. By culminating the complexities inherent in dysferlinopathies, this write up emphasizes the need for multidisciplinary approaches, precision medicine, and extensive collaboration in research and clinical trial design to advance our understanding and treatment of these challenging disorders.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.385

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 head0.034
GPT teacher head0.299
Teacher spread0.265 · 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 designTheoretical or conceptual
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

Citations14
Published2024
Admission routes2
Has abstractyes

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