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Record W2592016869 · doi:10.1194/jlr.r075556

Therapeutic potential of nuclear receptor agonists in Alzheimer's disease

2017· review· en· W2592016869 on OpenAlexfundno aff
Miguel Moutinho, Gary E. Landreth

Bibliographic record

VenueJournal of Lipid Research · 2017
Typereview
Languageen
FieldNeuroscience
TopicNuclear Receptors and Signaling
Canadian institutionsnot available
FundersNational Institute on AgingW. Garfield Weston FoundationWeston Brain Institute
KeywordsNeuroscienceDiseaseContext (archaeology)Nuclear receptorAlzheimer's diseaseMedicineTherapeutic approachInflammationTranscription factorBioinformaticsBiologyGeneInternal medicineGenetics

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is characterized by an extensive accumulation of amyloid-β (Aβ) peptide, which triggers a set of deleterious processes, including synaptic dysfunction, inflammation, and neuronal injury, leading to neuronal loss and cognitive impairment. A large body of evidence supports that nuclear receptor (NR) activation could be a promising therapeutic approach for AD. NRs are ligand-activated transcription factors that regulate gene expression and have cell type-specific effects. In this review, we discuss the mechanisms that underlie the beneficial effects of NRs in AD. Moreover, we summarize studies reported in the last 10-15 years and their major outcomes arising from the pharmacological targeting of NRs in AD animal models. The dissection of the pathways regulated by NRs in the context of AD is of importance in identifying novel and effective therapeutic strategies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.377
GPT teacher head0.481
Teacher spread0.104 · 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

Citations74
Published2017
Admission routes1
Has abstractyes

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