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Record W2791894605 · doi:10.3233/jad-179911

Metabolic Dysfunction in Alzheimer’s Disease: From Basic Neurobiology to Clinical Approaches

2018· review· en· W2791894605 on OpenAlexaff
Julia R. Clarke, Felipe C. Ribeiro, Rudimar Luiz Frozza, Fernanda G. De Felice, Mychael V. Lourenco

Bibliographic record

VenueJournal of Alzheimer s Disease · 2018
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsDiseaseNeuroscienceDementiaMedicineClinical trialBioinformaticsPathogenesisAlzheimer's diseasePsychologyBiologyPathology

Abstract

fetched live from OpenAlex

Clinical trials have extensively failed to find effective treatments for Alzheimer's disease (AD) so far. Even after decades of AD research, there are still limited options for treating dementia. Mounting evidence has indicated that AD patients develop central and peripheral metabolic dysfunction, and the underpinnings of such events have recently begun to emerge. Basic and preclinical studies have unveiled key pathophysiological mechanisms that include aberrant brain stress signaling, inflammation, and impaired insulin sensitivity. These findings are in accordance with clinical and neuropathological data suggesting that AD patients undergo central and peripheral metabolic deregulation. Here, we review recent basic and clinical findings indicating that metabolic defects are central to AD pathophysiology. We further propose a view for future therapeutics that incorporates metabolic defects as a core feature of AD pathogenesis. This approach could improve disease understanding and therapy development through drug repurposing and/or identification of novel metabolic targets.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.228
GPT teacher head0.419
Teacher spread0.191 · 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.

Study designOther design
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

Citations88
Published2018
Admission routes1
Has abstractyes

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