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Record W2920570521 · doi:10.1007/s00335-019-09793-5

Treating Rett syndrome: from mouse models to human therapies

2019· review· en· W2920570521 on OpenAlex

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

VenueMammalian Genome · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMECP2Rett syndromeHuman geneticsBiologyX-inactivationGeneBioinformaticsMutationGeneticsComputational biologyX chromosomePhenotype

Abstract

fetched live from OpenAlex

Rare diseases are very difficult to study mechanistically and to develop therapies for because of the scarcity of patients. Here, the rare neuro-metabolic disorder Rett syndrome (RTT) is discussed as a prototype for precision medicine, demonstrating how mouse models have led to an understanding of the development of symptoms. RTT is caused by mutations in the X-linked gene methyl-CpG-binding protein 2 (MECP2). Mecp2-mutant mice are being used in preclinical studies that target the MECP2 gene directly, or its downstream pathways. Importantly, this work may improve the health of RTT patients. Clinical presentation may vary widely among individuals based on their mutation, but also because of the degree of X chromosome inactivation and the presence of modifier genes. Because it is a complex disorder involving many organ systems, it is likely that recovery of RTT patients will involve a combination of treatments. Precision medicine is warranted to provide the best efficacy to individually treat RTT patients.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.050
GPT teacher head0.283
Teacher spread0.233 · 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