AIMC Topic: Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization

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Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models.

ACS chemical neuroscience
Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard ...

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification.

Small (Weinheim an der Bergstrasse, Germany)
Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascerta...

Molecular Networking and On-Tissue Chemical Derivatization for Enhanced Identification and Visualization of Steroid Glycosides by MALDI Mass Spectrometry Imaging.

Analytical chemistry
Spatial metabolomics describes the spatially resolved analysis of interconnected pathways, biochemical reactions, and transport processes of small molecules in the spatial context of tissues and cells. However, a broad range of metabolite classes (e....

Advances in mass spectrometry imaging for spatial cancer metabolomics.

Mass spectrometry reviews
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progr...

Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data.

Journal of the American Society for Mass Spectrometry
The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and...

Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics.

BMC bioinformatics
BACKGROUND: Metabolomics is a primary omics topic, which occupies an important position in both clinical applications and basic researches for metabolic signatures and biomarkers. Unfortunately, the relevant studies are challenged by the batch effect...

Collaborative Robot Precision Task in Medical Microbiology Laboratory.

Sensors (Basel, Switzerland)
This study focuses on the feasibility of collaborative robot implementation in a medical microbiology laboratory by demonstrating fine tasks using kinesthetic teaching. Fine tasks require sub-millimetre positioning accuracy. Bacterial colony picking ...

Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning.

Scientific reports
The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-o...

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints.

Analytical chemistry
Plasma exosomes have shown great potential for liquid biopsy in clinical cancer diagnosis. Herein, we present an integrated strategy for isolating and analyzing exosomes from human plasma rapidly and then discriminating different cancers excellently ...

Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning.

Nature medicine
Early use of effective antimicrobial treatments is critical for the outcome of infections and the prevention of treatment resistance. Antimicrobial resistance testing enables the selection of optimal antibiotic treatments, but current culture-based t...