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Record W4400909249 · doi:10.1073/pnas.2407546121

Early metformin treatment: An effective approach for targeting fragile X syndrome pathophysiology

2024· article· en· W4400909249 on OpenAlexafffund
Jung‐Hyun Choi, Laura Marsal-García, Eve Peraldi, Caleb Walters, Ziying Huang, Ilse Gantois, Nahum Sonenberg

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsMcGill University
FundersNational Research Foundation of KoreaCanadian Institutes of Health ResearchNational Research FoundationFRAXA Research Foundation
KeywordsFragile X syndromeMetforminFMR1Gene silencingPathophysiologyAutism spectrum disorderKinaseMedicineDYRK1AProtein kinase ANeuroscienceBiologyBioinformaticsAutismEndocrinologyGeneGeneticsFragile xPsychiatry

Abstract

fetched live from OpenAlex

Fragile X syndrome (FXS) is the most common genetic cause of autism spectrum disorder engendered by transcriptional silencing of the fragile X messenger ribonucleoprotein 1 ( FMR1 ) gene. Given the early onset of behavioral and molecular changes, it is imperative to know the optimal timing for therapeutic intervention. Case reports documented benefits of metformin treatment in FXS children between 2 and 14 y old. In this study, we administered metformin from birth to Fmr1 −/y mice which corrected up-regulated mitogen-2 activated protein kinase/extracellular signal-regulated kinase and mammalian/mechanistic target of rapamycin complex 1 signaling pathways and specific synaptic mRNA-binding targets of FMRP. Metformin rescued increased number of calls in ultrasonic vocalization and repetitive behavior in Fmr1 −/y mice. Our findings demonstrate that in mice, early-in-life metformin intervention is effective in treating FXS pathophysiology.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.283
Teacher spread0.263 · 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 designBench or experimental
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

Citations6
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicGenetics and Neurodevelopmental DisordersFrench-language works237,207