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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...

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of and Related Species.

Molecules (Basel, Switzerland)
, the causative agent of glanders, and , the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in ...

Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks.

Applied microbiology and biotechnology
Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology co...

Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning.

Molecules (Basel, Switzerland)
A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive cl...

Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand.

World journal of microbiology & biotechnology
Food poisoning from consumption of food contaminated with non-typhoidal Salmonella spp. is a global problem. A modified high resolution DNA melting curve analysis (m-HRMa) was introduced to provide effective discrimination among closely related HRM c...

Capabilities of GPT-4o and Gemini 1.5 Pro in Gram stain and bacterial shape identification.

Future microbiology
Assessing the visual accuracy of two large language models (LLMs) in microbial classification. GPT-4o and Gemini 1.5 Pro were evaluated in distinguishing Gram-positive from Gram-negative bacteria and classifying them as cocci or bacilli using 80 Gra...

Prediction of Clonal Complexes from Multilocus Variable Number Tandem Repeat Analysis Patterns Using a Machine Learning Approach.

Foodborne pathogens and disease
Multilocus variable number tandem repeat analysis (MLVA) is a molecular subtyping technique that remains useful for those without the resources to access whole genome sequencing for the tracking and tracing of bacterial contaminants. Unlike technique...

Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction.

Food chemistry
This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of fo...

Classification of subspecies based on MALDI-TOF MS protein profiles using machine learning models.

Microbiology spectrum
UNLABELLED: is an important bacterial species used as a starter culture for fermented foods; however, two subspecies within this species exhibit different properties in the foods. Matrix-assisted laser desorption/ionization-time of flight mass spect...