ReviewMetabolites2021
Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns.
Review in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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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.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Picosecond infrared laser mass spectrometry for 10-second identification of lymphoproliferative imposter tumours in patient-derived xenografts.Scientific reports · 2026Article
- A molecular array for 10-second diagnosis of common spinal tumor types with picosecond infrared laser mass spectrometry.Neuro-oncology · 2025Article
- Differential Mobility Spectrometry Authenticates the Botanical Origins of Five Monofloral Honeys.Journal of agricultural and food chemistry · 2025Article
- A Snapshot, Using a Multi-Omic Approach, of the Metabolic Cross-Talk and the Dynamics of the Resident Microbiota in Ripening Cheese Inoculated withFoods (Basel, Switzerland) · 2024Article
- A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes.International journal of molecular sciences · 2024Review
- Lipidomic-Based Approach to 10 s Classification of Major Pediatric Brain Cancer Types with Picosecond Infrared Laser Mass Spectrometry.Analytical chemistry · 2024Article
- Seasonal Variation in Raw Milk VOC Profile within Intensive Feeding Systems.Foods (Basel, Switzerland) · 2023Article
- Lipidome Analysis of Oropharyngeal Tumor Tissues Using Nanosecond Infrared Laser (NIRL) Tissue Sampling and Subsequent Mass Spectrometry.International journal of molecular sciences · 2023Article
- Metabolic signature ofFrontiers in veterinary science · 2023Article
- Authentication of Edible Insects' Powders by the Combination of DART-HRMS Signatures: The First Application of Ambient Mass Spectrometry to Screening of Novel Food.Foods (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.
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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.