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Record W2152695501 · doi:10.3389/fncel.2015.00055

Targeted pharmacological treatment of autism spectrum disorders: fragile X and Rett syndromes

2015· review· en· W2152695501 on OpenAlexafffund
Hansen Wang, Sandipan Pati, Lucas Pozzo‐Miller, Laurie C. Doering

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsMcMaster UniversityUniversity of TorontoCanada Research Chairs
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeNational Institutes of HealthRett Syndrome Research TrustInternational Rett Syndrome FoundationNational Natural Science Foundation of ChinaNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsRett syndromeAutismFragile X syndromeMedicineClinical trialFragile xNeuroscienceBioinformaticsPsychiatryPsychologyPathologyGeneticsGeneBiology

Abstract

fetched live from OpenAlex

Autism spectrum disorders (ASDs) are genetically and clinically heterogeneous and lack effective medications to treat their core symptoms. Studies of syndromic ASDs caused by single gene mutations have provided insights into the pathophysiology of autism. Fragile X and Rett syndromes belong to the syndromic ASDs in which preclinical studies have identified rational targets for drug therapies focused on correcting underlying neural dysfunction. These preclinical discoveries are increasingly translating into exciting human clinical trials. Since there are significant molecular and neurobiological overlaps among ASDs, targeted treatments developed for fragile X and Rett syndromes may be helpful for autism of different etiologies. Here, we review the targeted pharmacological treatment of fragile X and Rett syndromes and discuss related issues in both preclinical studies and clinical trials of potential therapies for the diseases.

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 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 score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.032
GPT teacher head0.290
Teacher spread0.258 · 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.

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

Citations25
Published2015
Admission routes2
Has abstractyes

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