Purpose: Staphylococcus epidermidis colonies often display several morphologies and antimicrobial susceptibility patterns when cultured from device-related infections, and may represent one or multiple genotypes. Genotyping may be helpful in the clinical interpretation, but is time consuming and expensive. We wanted to establish a method for rapid discrimination of S. epidermidis genotypes for use in a routine microbiology laboratory.
Methodology: A real-time PCR targeting eight discriminatory class I or II single-nucleotide polymorphisms (SNPs) in six of the seven housekeeping genes was constructed. Post PCR, high-resolution melt (HRM) analysis using EvaGreen as fluorophore discriminated amplicons based on their percentage GC content.
Results: In silico, 42 representative sequence types (STs), including all major MLST group and subgroup founders, were separated into 23 different cluster profiles with a Simpson's index of diversity of 0.97. By HRM-PCR, 11 commonly encountered hospital and outbreak STs were separated into eight HRM patterns.
Conclusion: This method can rapidly establish whether S. epidermidis strains belong to different genotypes. It can be used in patients with S. epidermidis infections, as an aid in outbreak investigations and to select strains for investigation with more discriminatory methods, saving workload and costs. Results may be obtained the same day as culture results. Its strength lies mainly in indicating differences, as some STs may have the same melt profile. Changes in S. epidermidis epidemiology may warrant alterations in the inclusion of SNPs. We believe this method can reduce the threshold for performing genotyping analysis on an increasingly important nosocomial pathogen.
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http://dx.doi.org/10.1099/jmm.0.000663 | DOI Listing |
Sci Rep
December 2024
School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Life Sciences Building 85, University Road, Highfield, Southampton, SO17 1BJ, UK.
Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need.
View Article and Find Full Text PDFInfect Genet Evol
December 2024
University Paris-Est, Anses, Animal health laboratory, Bacterial zoonosis unit, Maisons-Alfort, France. Electronic address:
Burkholderia pseudomallei, a soil-borne bacterium that causes melioidosis, endemic in South and Southeast Asia and northern Australia, is now emerging in new regions. Since the 1990s, cases have been reported in French overseas departments, including Martinique and Guadeloupe in the Caribbean, and Reunion Island and Mayotte in the Indian Ocean, suggesting a local presence of the bacterium. Our phylogenetic analysis of 111 B.
View Article and Find Full Text PDFToxins (Basel)
December 2024
Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Institute of Feed Research, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Beijing 100081, China.
Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety and well-being of animals. This study aims to develop a rapid and cost-effective method using an electronic nose (E-nose) and machine learning algorithms to predict whether ZEN levels in pet food exceed the regulatory limits (250 µg/kg), as set by Chinese pet food legislation.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, Sta. Catarina Martir, San Andrés Cholula 72810, Mexico.
Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images.
View Article and Find Full Text PDFBiosensors (Basel)
November 2024
EMBIO Diagnostics Ltd., Athalassas, 2018 Nicosia, Cyprus.
The prevalence of foodborne diseases is continuously increasing, causing numerous hospitalizations and deaths, as well as money loss in the agri-food sector and food supply chain worldwide. The standard analyses currently used for bacteria detection have significant limitations with the most important being their long procedural time that can be crucial for foodborne outbreaks. In this study, a biosensor system able to perform robust and accurate detection of spp.
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