AIMC Topic: Mass Spectrometry

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Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.

Nature communications
Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lip...

Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications.

Environment international
Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targ...

Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning.

Journal of breath research
Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic ...

Surface-Induced Unfolding Reveals Unique Structural Features and Enhances Machine Learning Classification Models.

Analytical chemistry
Native ion mobility-mass spectrometry combined with collision-induced unfolding (CIU) is a powerful analytical method for protein characterization, offering insights into structural stability and enabling the differentiation of analytes with similar ...

Advanced deep learning models for predicting elemental concentrations in iron ore mine using XRF data: a cost-effective alternative to ICP-MS methods.

Environmental geochemistry and health
Accurate elemental analysis is a critical requirement for mineral exploration, particularly in regions like Iran, where the mining sector has experienced a substantial increase in exploration activities over the past decade. Inductively Coupled Plasm...

QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.

Analytical chemistry
In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reli...

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.

Analytical chemistry
Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database sear...

Prediction of coffee traits by artificial neural networks and laser-assisted rapid evaporative ionization mass spectrometry.

Food research international (Ottawa, Ont.)
BACKGROUND: Coffee is an important commodity in the worldwide economy and smart technologies are important for accurate quality control and consumer-oriented product development. Sensory perception is probably the most important information since it ...

iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.

Nature communications
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files fro...

Untargeted Swab Touch Spray-Mass Spectrometry Analysis with Machine Learning for On-Site Breast Surgical Margin Assessment.

Analytical chemistry
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 t...