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Record W2754780832 · doi:10.1177/1536012117717852

Molecular Imaging of Hydrolytic Enzymes Using PET and SPECT

2017· review· en· W2754780832 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

VenueMolecular Imaging · 2017
Typereview
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsThunder Bay Regional Research InstituteUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsPositron emission tomographyEnzymeHydrolysisMolecular imagingChemistryEmission computed tomographyPet imagingSingle-photon emission computed tomographyBiochemistryNuclear medicineMedicineBiologyIn vivo

Abstract

fetched live from OpenAlex

Hydrolytic enzymes are a large class of biological catalysts that play a vital role in a plethora of critical biochemical processes required to maintain human health. However, the expression and/or activity of these important enzymes can change in many different diseases and therefore represent exciting targets for the development of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) radiotracers. This review focuses on recently reported radiolabeled substrates, reversible inhibitors, and irreversible inhibitors investigated as PET and SPECT tracers for imaging hydrolytic enzymes. By learning from the most successful examples of tracer development for hydrolytic enzymes, it appears that an early focus on careful enzyme kinetics and cell-based studies are key factors for identifying potentially useful new molecular imaging agents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.406
Teacher spread0.349 · 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