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Record W2169224435 · doi:10.2310/7290.2014.00057

Synthesis and Evaluation of Optical and PET GLP-1 Peptide Analogues for GLP-1R Imaging

2015· article· en· W2169224435 on OpenAlexafffund
Babak Behnam Azad, Vanessa Rota, Lihai Yu, Rebecca McGirr, André H. St. Amant, Ting-Yim Lee, Savita Dhanvantari, Leonard G. Luyt

Bibliographic record

VenueMolecular Imaging · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLawson Health Research Institute
FundersCanadian Institutes of Health Research
KeywordsPeptideGlucagon-like peptide-1ChemistryOptical imagingBiophysicsBiochemistryMedicineBiologyEndocrinologyPhysicsOptics

Abstract

fetched live from OpenAlex

A fluorescein-GLP-1 (7-37) analog was generated to determine GLP-1R distribution in various cell types of the pancreas in both strains of mice and receptor-specific uptake was confirmed by blocking with exendin-4. Biodistribution studies were carried out using 68Ga-labeled GLP-1(7-37) peptides in CD1 and C57BL/6 mice. In addition, immunocompromised mice bearing GLP-1R-expressing insulinomas were evaluated by positron emission tomography (PET) imaging and ex vivo biodistribution studies. The optical GLP-1 probe strongly colocalized with immunofluorescence for insulin and glucagon, and more weakly with amylase (exocrine pancreas) and cytokeratin 19 (ductal cells), confirming its application for in situ GLP-1R imaging in various pancreatic cell types. Insulinomas were clearly visualized by in vivo PET. Reducing the peptide positive charge decreased renal retention as well as tumor uptake. Results demonstrate the application of the developed GLP-1 peptide analogues for in situ (optical) and in vivo (PET) imaging of GLP-1R expression.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.026
GPT teacher head0.302
Teacher spread0.276 · 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 designBench or experimental
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

Citations10
Published2015
Admission routes2
Has abstractyes

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