AIMC Topic: Mass Spectrometry

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Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Nature communications
Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico g...

Prediction of clinical outcomes of ST-elevated myocardial infarction patients using atmospheric solids analysis probe mass spectrometry and machine learning.

The Analyst
: Analysis of small molecule metabolites found in blood plasma of patients undergoing treatment for STEMI has the potential to be used as a clinical diagnostic and prognostic tool, capable of predicting disease progression, risk of negative outcomes,...

Machine learning-enhanced direct mass spectrometry analysis of non-volatile breath metabolites for rapid and accurate lung cancer screening.

Analytical methods : advancing methods and applications
Breath analysis by direct mass spectrometry faces significant challenges due to the inherent complexities in sample collection, low analyte concentrations, and accurate compound identification. While current breath analysis primarily focuses on volat...

The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis.

Nature communications
Lyme neuroborreliosis (LNB), a nervous system infection caused by tick-borne spirochetes of the Borrelia burgdorferi sensu lato complex, is among the most frequent bacterial infections of the nervous system in Europe. Early diagnosis and continuous m...

Mass spectrometry combined with machine learning identifies novel protein signatures as demonstrated with multisystem inflammatory syndrome in children.

Scientific reports
Rapid and accurate diagnosis of emerging inflammatory illnesses is challenging due to overlapping clinical features with existing conditions. We demonstrate an approach that integrates proteomic analysis with machine learning to identify diagnostic p...

Practical Guidance for Training Machine Learning Models in Metabolomics and Mass Spectrometry Research.

Analytical chemistry
This tutorial offers a step-by-step guide for analytical chemists to train machine learning models for MS-based metabolomics. It covers data preparation, feature engineering, model selection, evaluation, and interpretation, along with real-world exam...

Evaluation of ion mobility, uni- and multidimensional liquid chromatography for non-target screening of phenolic compounds in wheat flag leaves.

Journal of chromatography. A
Non-target screening (NTS) of plant secondary metabolites is analytically challenging due to the complexity of mixtures with structurally similar compounds and isomers. This study evaluates the added value of ion mobility spectrometry (IMS) and compr...

Determination of geographical origin of Hovenia dulcis found in Korea and China via inorganic element analysis using inductively coupled plasma spectroscopy and multivariate statistical analysis.

Food chemistry
The fruit of Hovenia dulcis, a popular Korean hangover remedy, is marketed as Korean and Chinese products. To protect domestic producers, ensure accurate labeling, and safeguard consumers, this study discriminated origin through inorganic element ana...

DeePFAS: Deep-Learning-Enabled Rapid Annotation of PFAS: Enhancing Nontargeted Screening through Spectral Encoding and Latent Space Analysis.

Environmental science & technology
Detecting PFAS is challenging due to their diverse chemical structures, lack of standards, complex sample matrices, and the need for sensitive equipment to measure trace levels. Background contamination and the sheer number of PFAS further hinder the...