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Record W2107055653 · doi:10.18433/j3mk59

Effects of Rivastigmine and Donepezil on Brain Acetylcholine Levels in Acetylcholinesterase-Deficient Mice

2009· article· en· W2107055653 on OpenAlexvenueno aff
Runa S. Naik, Joachim Hartmann, Cornelia Kiewert, Ellen G. Duysen, Oksana Lockridge, Jochen Klein

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2009
Typearticle
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAcetylcholinesteraseButyrylcholinesteraseRivastigmineDonepezilAcetylcholineAchéCholinesteraseChemistryPharmacologyCholinergicInternal medicineEndocrinologyMedicineBiochemistryEnzymeDementia

Abstract

fetched live from OpenAlex

PURPOSE: Alzheimer s disease is characterized by a dysfunction of central cholinergic systems and is treated by inhibitors of acetylcholinesterase (AChE). This study tests the effect of two AChE inhibitors in therapeutic use, rivastigmine and donepezil, in mice that are devoid of AChE (AChE-/- mice). Rivastigmine is an inhibitor of both AChE and butyrylcholinesterase (BChE) whereas donepezil is a selective inhibitor of AChE. METHODS: We have used in vivo microdialysis to investigate the effects of the two drugs on the extracellular concentration of acetylcholine (ACh) in the hippocampus of AChE-/- mice. RESULTS: Extracellular ACh levels in the hippocampus were 30-fold elevated in AChE-/- mice compared to wild-type (AChE+/+) animals. Infusion of rivastigmine (1 and 10 microM) caused a further doubling of ACh levels in AChE-/- mice within 90-120 min. In contrast, infusion of donepezil (1 microM) did not affect hippocampal ACh levels in AChE-/- mice although it increased ACh levels more than twofold in wild-type mice. CONCLUSIONS: In the absence of AChE, rivastigmine enhances the levels of extracellular ACh by inhibiting BChE. This finding may be of therapeutic relevance because BChE activity is preserved, but AChE activity is strongly decreased, in late-stage Alzheimer s disease.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.072
GPT teacher head0.420
Teacher spread0.348 · 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

Citations51
Published2009
Admission routes1
Has abstractyes

Explore more

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