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Memantine: Updating a rare success story in pro-cognitive therapeutics

2023· review· en· W4387538360 on OpenAlexafffund
Parisa Karimi Tari, Chris G. Parsons, Graham L. Collingridge, Gerhard Rammes

Bibliographic record

VenueNeuropharmacology · 2023
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsOccupational Cancer Research CentreUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersCanadian Institutes of Health ResearchBundesministerium für Bildung und ForschungKrembil FoundationEli Lilly and Company
KeywordsMemantineDruggabilityCognitionNeuroscienceNMDA receptorMedicinePsychologyClinical trialPharmacologyReceptorBiologyInternal medicine

Abstract

fetched live from OpenAlex

The great potential for NMDA receptor modulators as druggable targets in neurodegenerative disorders has been met with limited success. Considered one of the rare exceptions, memantine has consistently demonstrated restorative and prophylactic properties in many AD models. In clinical trials memantine slows the decline in cognitive performance associated with AD. Here, we provide an overview of the basic properties including pharmacological targets, toxicology and cellular effects of memantine. Evidence demonstrating reductions in molecular, physiological and behavioural indices of AD-like impairments associated with memantine treatment are also discussed. This represents both an extension and homage to Dr. Chris Parson's considerable contributions to our fundamental understanding of a success story in the AD treatment landscape.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0040.003

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 head0.260
GPT teacher head0.488
Teacher spread0.228 · 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 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

Citations67
Published2023
Admission routes2
Has abstractno

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