Evidence map›Paper›PMID 31548988›Full record

ArticleScience advances2019

3D-printed automation for optimized PET radiochemistry.

Alejandro Amor-Coarasa, James M Kelly, John W Babich

Abstract read
In one paragraph

Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alejandro Amor-CoarasaDivision of Radiopharmaceutical Sciences and Molecular Imaging Innovations Institute (MI), Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA.ORCID 0000-0002-6793-6765
James M KellyDivision of Radiopharmaceutical Sciences and Molecular Imaging Innovations Institute (MI), Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA.ORCID 0000-0002-0525-0047
John W BabichDivision of Radiopharmaceutical Sciences and Molecular Imaging Innovations Institute (MI), Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA.ORCID 0000-0002-1616-3416

Funding

Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Clinical and Translational Science CenterUL1TR000457 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI IMPERATO-MCGINLEY, JULIANNE L · 2012 to 2016
$46.0M
NCATS NIH HHS UL1 TR000457NCATS NIH HHS UL1 TR002384
6 · The paper itself

Abstract

Reproducible batch synthesis of radioligands for imaging by positron emission tomography (PET) in a manner that maximizes ligand yield, purity, and molar activity, and minimizes cost and exposure to radiation, remains a challenge, as new and synthetically complex radioligands become available. Commercially available automated synthesis units (ASUs) solve many of these challenges but are costly to install and cannot always accommodate diverse chemistries. Through a reiterative design process, we exploit the proliferation of three-dimensional (3D) printing technologies to translate optimized reaction conditions into ASUs composed of 3D-printed, electronic, and robotic parts. Our units are portable and robust and reduce radiation exposure, shorten synthesis time, and improve the yield of the final radiopharmaceutical for a fraction of the cost of a commercial ASU. These 3D-printed ASUs highlight the gains that can be made by designing a fit-for-purpose ASU to accommodate a synthesis over accommodating a synthesis to an unfit ASU.

Indexed as

AutomationPositron-Emission TomographyPrinting, Three-Dimensional

Identifiers

PMID31548988
PMCPMC6744267

What OpenQuestion holds

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Registered trials

None linked

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.