Publications

Publications

Automated radiosynthesis of no-carrier-added 4-[18F]fluoroiodobenzene: a versatile building block in 18F radiochemistry.

By:
Contributors: Frank Wuest Research Group
J Labelled Comp Radiopharm. 2014 Feb;57(2):104-9.

Abstract

4-[18F]Fluoroiodobenzene ([18F]FIB) is a versatile building block in 18F radiochemistry used in various transition metal-mediated C-C and C-N cross-coupling reactions and [18F]fluoroarylation reactions. Various synthesis routes have been described for the preparation of [18F]FIB. However, to date, no automated synthesis of [18F]FIB has been reported to allow access to larger amounts of [18F]FIB in high radiochemical and chemical purity. Herein, we describe an automated synthesis of no-carrier-added [18F]FIB on a GE TRACERlab™ FX automated synthesis unit starting from commercially available(4-iodophenyl)diphenylsulfonium triflate as the labelling precursor. [18F]FIB was prepared in high radiochemical yields of 89 ± 10% (decay-corrected, n = 7) within 60 min, including HPLC purification. The radiochemical purity exceeded 95%, and specific activity was greater than 40 GBq/μmol. Typically, from an experiment, 6.4 GBq of [18F]FIB could be obtained starting from 10.4 GBq of [18F]fluoride.

 

PubMed

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goes to…APCaRI member Russ Greiner

Image of DREAM challenge winners, Russ Greiner pictured on far left.

Dr. Russ Greiner, Canada CIFAR AI Chair, Fellow-in-Residence at Amii, University of Alberta Professor, and APCaRI member, received the CAIAC Lifetime Achievement Award announced at the Canadian AI Conference on May 27, 2021. This the highest honour bestowed by CAIAC, given in recognition to researchers who have distinguished themselves through outstanding research excellence in AI during the course of their academic career. APCaRI congratulates Russ Greiner for his well-deserved CAIAC Lifetime Achievement Award!

“Using machine learning techniques to produce effective, evidence-based personalized treatment”

The main foci of Russ Greiner’s current work are (1) bioinformatics and medical informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability. He has published over 200 refereed papers and patents, most in the areas of machine learning and knowledge representation, including 4 that have been awarded Best Paper prizes.

One of these four papers was an entry into an international machine learning competition hosted by DREAM, an open-science effort dedicated to improving health and health care through crowdsourcing problem-solving. DREAM’s challenge was to develop an algorithm to predict which prostate cancer patients would respond to certain treatments and which would follow the medication regimen. The algorithm could be used by clinicians to help chose the best treatment plans for the patient.

Greiner and a team of students tied for the top place in the competition against over 50 teams from around the world. Then the winners collaborated to create an even better solution to the problem!

 

 

 

 

 

 

- Perrin Beatty