Modern machine-learning applications in ambient ionization mass spectrometry.

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis.

Incorporation of a Disposable ESI Emitter into Inline Cartridge Extraction Mass Spectrometry Improves Throughput and Spectra Stability.

Rapid and reliable methods for detecting tumor margins are crucial for neuro-oncology. Several mass spectrometry-based methods have been recently proposed to address this problem. Inline Cartridge Extraction (ICE) demonstrates the potential for clinical application, based on ex-vivo analysis of dissected tissues, but requires time-consuming steps to avoid cross-contamination.

Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry.

Tumor cell percentage (TCP) is an essential characteristic of biopsy samples that directly affects the sensitivity of molecular testing in clinical practice. Apart from clarifying diagnoses, rapid evaluation of TCP combined with various neuronavigation systems can be used to support decision making in neurosurgery. It is known that ambient mass spectrometry makes it possible to rapidly distinguish healthy from malignant tissues.

Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues.

Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast identification of dissected tissues could assist the neurosurgery procedure.

The software for interactive evaluation of mass spectra stability and reproducibility.

Summary: Mass spectrometry (MS) methods are widely used for the analysis of biological and medical samples. Recently developed methods, such as DESI, REIMS and NESI allow fast analyses without sample preparation at the cost of higher variability of spectra. In biology and medicine, MS profiles are often used with machine learning (classification, regression, etc.