ArticleJournal of visualized experiments : JoVE2021
Optimization of Radiochemical Reactions using Droplet Arrays.
Article in Journal of visualized experiments : JoVE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Current automated radiosynthesizers are designed to produce large clinical batches of radiopharmaceuticals. They are not well suited for reaction optimization or novel radiopharmaceutical development since each data point involves significant reagent consumption, and contamination of the apparatus requires time for radioactive decay before the next use. To address these limitations, a platform for performing arrays of miniature droplet-based reactions in parallel, each confined within a surface-tension trap on a patterned polytetrafluoroethylene-coated silicon "chip", was developed. These chips enable rapid and convenient studies of reaction parameters including reagent concentrations, reaction solvent, reaction temperature and time. This platform permits the completion of hundreds of reactions in a few days with minimal reagent consumption, instead of taking months using a conventional radiosynthesizer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.