Membrane Fusion-Assisted Laser Desorption/Ionization Mass Spectrometry for In Situ Extracellular Vesicle Metabolic Fingerprinting.
Machine learning identified nine metabolite biomarkers in blood that distinguished pancreatic cancer patients from healthy controls with high accuracy.
Metabolites packaged within extracellular vesicles (EVs) are increasingly recognized for their role in intercellular communication and metabolic regulation, making them attractive liquid-biopsy targets. Combining machine learning with MF-LDI-MS, the platform successfully discriminated pancreatic cancer patients from healthy controls in 144 plasma samples with 92.1% diagnostic accuracy, identifying nine candidate metabolite biomarkers.
What the study was
- Study design
- Cohort study
- Population
- Cancer/disease patients
- Category
- Early Detection
- Maturity
- Validated
- Journal
- Analytical chemistry
Why it surfaced
Matched topic(s): Early cancer detection. Study design: Cohort study. Score 6/10.
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