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Record W2997194916

Searching for novel PET radiotracers: imaging cardiac perfusion, metabolism and inflammation.

2018· review· en· W2997194916 on OpenAlexaff
Caitlund Davidson, Christopher P. Phenix, T.C. Tai, Neelam Khaper, Simon J. Lees

Bibliographic record

VenuePubMed · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicS100 Proteins and Annexins
Canadian institutionsLaurentian UniversityNOSM UniversityUniversity of SaskatchewanLakehead University
Fundersnot available
KeywordsMedicineInflammationCardiac PETPositron emission tomographyCardiac imagingPerfusionImmune systemCardiac function curveMolecular imagingDiseaseFluorodeoxyglucosePathologyNuclear medicineRadiologyCardiologyInternal medicineImmunologyIn vivoHeart failure
DOInot available

Abstract

fetched live from OpenAlex

F-FDG has many ideal characteristics of a radiotracer but lacks the ability to differentiate between glucose uptake in normal cardiomyocytes and inflammatory cells. Developing a PET radiotracer that differentiates not only between inflammatory cells and normal cardiomyocytes, but between types of immune cells involved in inflammation would be ideal. This article reviews current PET radiotracers used in cardiac imaging, their limitations, and potential radiotracer candidates for imaging cardiac inflammation in early stages of development of acute and chronic cardiac diseases. The select radiotracers reviewed have been tested in animals and/or show potential to be developed as a radiotracer for the detection of cardiac inflammation by targeting the enzymatic activities or subpopulations of macrophages that are recruited to an injured or infected site.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.998
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.284
Teacher spread0.255 · 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.

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

Citations34
Published2018
Admission routes1
Has abstractyes

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