AIMC Topic: Bacterial Typing Techniques

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Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hypers...

FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Identification of Pathogenic Bacteria.

Analytical chemistry
Identification of microorganisms by Fourier transform infrared (FT-IR) spectroscopy is known as a promising alternative to conventional identification techniques in clinical, food, and environmental microbiology. In this study we demonstrate the appl...

Discrimination of Klebsiella pneumoniae and Klebsiella quasipneumoniae by MALDI-TOF Mass Spectrometry Coupled With Machine Learning.

MicrobiologyOpen
Klebsiella species, including Klebsiella pneumoniae and Klebsiella quasipneumoniae, present significant challenges in clinical microbiology due to their genetic similarity, which complicates accurate species identification using established methods, ...

Rapid Identification and Typing of Carbapenem-Resistant Klebsiella pneumoniae Using MALDI-TOF MS and Machine Learning.

Microbial biotechnology
Use matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) to screen the specific mass peaks of carbapenem-resistant Klebsiella pneumoniae (CRKP), compare the differences in spectrum peaks between intestinal and b...