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Record W2558756599 · doi:10.1155/2016/7810196

Lamotrigine Reduces Inflammatory Response and Ameliorates Executive Function Deterioration in an Alzheimer’s-Like Mouse Model

2016· article· en· W2558756599 on OpenAlexafffund
Kexin Wang, Alejandro Fernandez-Escobar, Shuhong Han, Ping Zhu, Junhui Wang, Yu Sun

Bibliographic record

VenueBioMed Research International · 2016
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Toronto
FundersUniversity of ManitobaShantou UniversityLi Ka Shing Foundation
KeywordsLamotrigineMedicineProinflammatory cytokineNeuroinflammationEpilepsyPharmacologyDiseaseNeurosciencePsychologyImmunologyInternal medicinePsychiatryInflammation

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) has been described in the literature, to be associated with impairment of executive function which develops early in the course of disease, and an effective treatment for this clinical feature remains elusive. Preclinical studies have implied that lamotrigine, an antiepileptic agent, could be a potential treatment for executive dysfunction in AD patients. Although there have been promising results in previous studies with lamotrigine, executive function has never been measured using animal models. The aim of the present study was to evaluate the effects of lamotrigine on executive function and determine whether lamotrigine can attenuate inflammatory response in an AD mouse model. Nontransgenic and transgenic mice were treated with lamotrigine (0 or 30 mg/kg/day) in a standard laboratory chow diet starting at 3 months of age. After 6 months of continuous lamotrigine administration, there was a marked improvement in executive function and a significant attenuation in the expression of proinflammatory cytokines. These results suggest that lamotrigine could ameliorate executive dysfunction and brain inflammatory response in the mouse model of AD and early lamotrigine intervention may be a promising therapeutic strategy for AD.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.101
GPT teacher head0.407
Teacher spread0.305 · 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.

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

Citations21
Published2016
Admission routes2
Has abstractyes

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