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Record W7005302377

A pre-clinical study of a therapy for Rett Syndrome by using a relevant transgenic mouse model

2023· dissertation· en· W7005302377 on OpenAlexfundno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchResearch Manitoba
KeywordsMutationAtaxiaCentral nervous systemDrugCistronGene expression
DOInot available

Abstract

fetched live from OpenAlex

Rett Syndrome (RTT) is a severe neurodevelopmental disorder mainly affecting females. After a normal developmental period, symptoms appear at the age of 6-18 months, including developmental regression and loss of learned skills such as speech and purposeful hand movements. De novo mutations in the Methyl CpG Binding Protein 2 (MECP2) gene, which codes the MeCP2 protein are the underlying cause of over 95 % of RTT cases. MeCP2 is an epigenetic reader of methylated DNA, which controls gene expression in various cell types of the brain, mainly neurons. The control of MECP2 gene dosage expression is critical since over-expression and under-expression, or genetic mutations lead to neurological deficits including MECP2 Duplication Syndrome (MDS) and RTT, which currently do not have any cure. The two main RTT-associated molecular signaling abnormalities such as impaired MECP2-BDNF-miR132 homeostasis and compromised mammalian Target of the Rapamycin (mTOR) pathway have been detected. Metformin (an anti-diabetic drug) is an inducer of MECP2E1/BDNF transcripts in brain cells in vitro, and an inhibitor of the hepatic mTOR pathway. However, it is unclear whether these effects of metformin can be detected in murine brain tissues. Furthermore, due to the drug safety profile, low price, and penetrability of metformin through the blood-brain barrier, it has been re-purposed in various neurodevelopmental disorders. As a result, metformin may regulate RTT-associated abnormalities and improve RTT-like phenotypes of an RTT mouse model. Consequently, in my thesis, I aim to reveal the gap in knowledge regarding the Mecp2-deficient abnormalities at the basal level as well as after metformin treatment for therapeutic purposes. To accomplish these aims, metformin, and vehicle treatments in Wild Type mice, monitoring of body weight, measurement of blood glucose level, and tissue collection for molecular analysis were completed. The results confirmed the safety of metformin treatment through intraperitoneal injection, and its sex- and region-dependent effects at the molecular level. Furthermore, metformin treatments in mutant mice showed an age- and sex-dependent improvement of the RTT-like symptoms at the phenotypical and behavioral levels. In conclusion, our study provides proof of principle for the effect of metformin on RTT-like abnormalities at the basal level and its therapeutic effects at the molecular, phenotypical, and behavioral levels in mice. These results offer an important insight for future re-purposing of metformin for the treatment of neurological disorders including RTT.

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)0.000
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.052
GPT teacher head0.334
Teacher spread0.283 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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