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Record W4391169665 · doi:10.3390/brainsci14020120

Rett Syndrome and the Role of MECP2: Signaling to Clinical Trials

2024· review· en· W4391169665 on OpenAlexafffund
Angelica Maria Assunçao da Ponte Lopes, Sampath K. Loganathan, Jayalakshmi Caliaperumal

Bibliographic record

VenueBrain Sciences · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersInternational Development Research CentreCanadian Institutes of Health ResearchCancer Research Society
KeywordsRett syndromeMECP2Neurodevelopmental disorderAutismApraxiaNeuroscienceMedicinePsychologyPhenotypeBiologyAphasiaGeneGeneticsPsychiatry

Abstract

fetched live from OpenAlex

Rett syndrome (RTT) is a neurological disorder that mostly affects females, with a frequency of 1 in 10,000 to 20,000 live birth cases. Symptoms include stereotyped hand movements; impaired learning, language, and communication skills; sudden loss of speech; reduced lifespan; retarded growth; disturbance of sleep and breathing; seizures; autism; and gait apraxia. Pneumonia is the most common cause of death for patients with Rett syndrome, with a survival rate of 77.8% at 25 years of age. Survival into the fifth decade is typical in Rett syndrome, and the leading cause of death is cardiorespiratory compromise. Rett syndrome progression has multiple stages; however, most phenotypes are associated with the nervous system and brain. In total, 95% of Rett syndrome cases are due to mutations in the MECP2 gene, an X-linked gene that encodes for the methyl CpG binding protein, a regulator of gene expression. In this review, we summarize the recent developments in the field of Rett syndrome and therapeutics targeting MECP2.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.133
GPT teacher head0.448
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2024
Admission routes2
Has abstractyes

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