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Record W2071362951 · doi:10.1038/jcbfm.2012.203

Voxel-Based Imaging of Translocator Protein 18Kda (TSPO) in High-Resolution PET

2013· article· en· W2071362951 on OpenAlexafffund
Ji Hyun Ko, Yuko Koshimori, Romina Mizrahi, Pablo Rusjan, Alan A. Wilson, Anthony E. Lang, Sylvain Houle, Antonio P. Strafella

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2013
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsToronto Western HospitalUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsTranslocator proteinVoxelNuclear medicineMicrogliaIn vivoChemistryLigand (biochemistry)RadioligandPet imagingPositron emission tomographyCompartment (ship)Binding potentialNuclear magnetic resonanceBiomedical engineeringMedicineReceptorRadiologyBiologyInternal medicineBiochemistryPhysicsInflammation

Abstract

fetched live from OpenAlex

In vivo imaging of translocator protein 18 kDa (TSPO) has received significant attention as potential biomarker of microglia activation. Several radioligands have been designed with improved properties. Our group recently developed an (18)F-labeled TSPO ligand, [(18)F]-FEPPA, and confirmed its reliability with a 2-tissue compartment model. Here, we extended, in a group of healthy subjects, its suitability for use in voxel-based analysis with the newly proposed graphical analysis approach, Relative-Equilibrium-Gjedde-Patlak (REGP) plot. The REGP plot successfully replicated the total distribution volumes estimated by the 2-tissue compartment model. We also showed its proof-of-concept in a patient with possible meningioma showing increased [(18)F]-FEPPA total distribution volume.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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 designObservational
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

Citations12
Published2013
Admission routes2
Has abstractyes

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Same venueJournal of Cerebral Blood Flow & MetabolismSame topicRadiopharmaceutical Chemistry and ApplicationsFrench-language works237,207