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Record W4317425760 · doi:10.3390/genes14020254

Towards a Cure for HARS Disease

2023· review· en· W4317425760 on OpenAlexafffund
Sarah D. P. Wilhelm, Rosan Kenana, Yi Qiu, Patrick O’Donoghue, Ilka U. Heinemann

Bibliographic record

VenueGenes · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAminoacyl tRNA synthetaseHistidineBiologyTransfer RNAAmino acidGeneticsGeneRNA

Abstract

fetched live from OpenAlex

Histidyl-tRNA synthetase (HARS) ligates histidine to its cognate transfer RNA (tRNAHis). Mutations in HARS cause the human genetic disorders Usher syndrome type 3B (USH3B) and Charcot-Marie-Tooth syndrome type 2W (CMT2W). Treatment for these diseases remains symptomatic, and no disease specific treatments are currently available. Mutations in HARS can lead to destabilization of the enzyme, reduced aminoacylation, and decreased histidine incorporation into the proteome. Other mutations lead to a toxic gain-of-function and mistranslation of non-cognate amino acids in response to histidine codons, which can be rescued by histidine supplementation in vitro. We discuss recent advances in characterizing HARS mutations and potential applications of amino acid and tRNA therapy for future gene and allele specific therapy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.078
GPT teacher head0.354
Teacher spread0.276 · 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 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

Citations22
Published2023
Admission routes2
Has abstractyes

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