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Record W1994608908 · doi:10.1002/ddr.20290

Small molecule inhibitors of Aβ‐aggregation and neurotoxicity

2009· article· en· W1994608908 on OpenAlexaff
Cheryl A. Hawkes, Vivian Ng, JoAnne McLaurin

Bibliographic record

VenueDrug Development Research · 2009
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsBasal forebrainNeuroscienceNeurotoxicityCholinergic neuronCholinergicAmyloid (mycology)DrugExtracellularForebrainAlzheimer's diseasePharmacologyChemistryDiseaseMedicinePsychologyPathologyToxicityBiochemistryInternal medicineCentral nervous system

Abstract

fetched live from OpenAlex

Abstract Alzheimer disease (AD) is characterized pathologically by extracellular amyloid deposits composed of Aβ peptide, neurofibrillary tangles (NFTs) made up of hyperphosphorylated tau, and a deficit of cholinergic neurons in the basal forebrain. Presently, only symptomatic therapies are available for the treatment of AD and these therapies have a limited time frame of utility. Amyloid disorders represent the effects of chronic Aβ production and are not a secondary pathological effect caused by a distant trigger; therefore targeting Aβ is a viable pursuit. In this review, we will discuss the various small molecule anti‐aggregation inhibitors that have been reported in the literature, with emphasis on compounds that are presently being investigated in clinical trials. Drug Dev Res 70, 2009. © 2009 Wiley‐Liss, Inc.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.068
GPT teacher head0.354
Teacher spread0.286 · 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

Citations102
Published2009
Admission routes1
Has abstractyes

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