ArticleJournal of the American Society for Mass Spectrometry2024
Text Mining and Computational Chemistry Reveal Trends in Applications of Laser Desorption/Ionization Techniques to Small Molecules.
Article in Journal of the American Society for Mass Spectrometry, 2024. 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
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Continued development of laser desorption/ionization (LDI) since its inception in the 1960s has produced an explosion of soft ionization techniques, where ionization is assisted by the physical or chemical properties of a structure or matrix. While many of these techniques have primarily been used to ionize large biomolecules, including proteins, some have recently seen increasing applications to small molecules such as pharmaceuticals. Small molecules pose particular challenges for LDI techniques, including interference from the matrix or support in the low mass range. To investigate trends in the application of soft LDI techniques to small molecules, we combined text mining and computational chemistry, looking specifically at matrix substances, analyte properties, and the research domain. In addition to making visible the history of LDI techniques, the results may inform the choice of method and suggest new avenues of method development. All software and collected data are freely available on GitHub (https://github.com/ReinV/SCOPE), VOSviewer (https://www.vosviewer.com), and OSF (https://osf.io/zkmua/).
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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.