ArticleAnalytical chemistry2025
Untargeted Swab Touch Spray-Mass Spectrometry Analysis with Machine Learning for On-Site Breast Surgical Margin Assessment.
Article in Analytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Precision Medicine and Artificial Intelligence in Next-Generation Cancer Surgery: A Comprehensive Analysis of Clinical Applications, Therapeutic Outcomes, and Implementation Strategies.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Direct sampling mass spectrometry (MS) has rapidly advanced with the development of ambient ionization MS techniques. Swab touch-spray (TS)-MS has shown promise for rapid clinical diagnostics. However, commercially available swabs are notorious for their high background signals, particularly in the positive ionization mode. Although changes to MS methods or precleaning of the swabs can serve as workarounds, this inherent issue still limits the clinical application of swab TS-MS. In this study, we report the use of the sterile-packaged OmniSwab as an alternative material for untargeted swab TS-MS analysis. As a proof of concept, breast surgical margins were swabbed
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Registered trials
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