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Record W2122386516 · doi:10.2174/1389450111314070006

Peroxisome Proliferator-Activated Receptor (PPAR) Agonists as Promising New Medications for Drug Addiction: Preclinical Evidence

2013· review· en· W2122386516 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Drug Targets · 2013
Typereview
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Institutes of Health
KeywordsPeroxisome proliferator-activated receptorAddictionPPAR agonistPharmacologyReceptorNicotineMedicineDrugInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

This review examines the growing literature on the role of peroxisome proliferator-activated receptors (PPARs) in addiction. There are two subtypes of PPAR receptors that have been studied in addiction: PPAR-α and PPAR-γ. The role of each PPAR subtype in common models of addictive behavior, mainly pre-clinical models, is summarized. In particular, studies are reviewed that investigated the effects of PPAR-α agonists on relapse, sensitization, conditioned place preference, withdrawal and drug intake, and effects of PPAR-γ agonists on relapse, withdrawal and drug intake. Finally, studies that investigated the effects of PPAR agonists on neural pathways of addiction are reviewed. Taken together these preclinical data indicate that PPAR agonists are promising new medications for drug addiction treatment.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.004

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 head0.192
GPT teacher head0.435
Teacher spread0.243 · 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