Characterization of Klebsiella isolates by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and determination of antimicrobial resistance with VITEK 2 advanced expert system (AES)

Karagöz, Alper; Acar, Sumeyra; Körkoca, Hanifi

doi:10.3906/sag-1401-32

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…An automated plate-reading system was utilized to detect bacterial growth on an agar plate of a urine culture with 97.1% sensitivity and 93.6% specificity and to identify bacterial species based on colony morphology on chromogenic agar with 99.7% accuracy [ 71 ]. Moreover, an advanced expert system was applied to interpret drug-resistance profiles [ 72 ]. Finally, dashboards were used in the post-analytical process to summarize analytical results to facilitate the determination of patient intervention strategies by physicians.…”

Section: Advanced Approaches For Medical Parasite and Arthropod Diagnosesmentioning

confidence: 99%

State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

Ruenchit

2021

Diagnostics

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Conventional methods such as microscopy have been used to diagnose parasitic diseases and medical conditions related to arthropods for many years. Some techniques are considered gold standard methods. However, their limited sensitivity, specificity, and accuracy, and the need for costly reagents and high-skilled technicians are critical problems. New tools are therefore continually being developed to reduce pitfalls. Recently, three state-of-the-art techniques have emerged: DNA barcoding, geometric morphometrics, and artificial intelligence. Here, data related to the three approaches are reviewed. DNA barcoding involves an analysis of a barcode sequence. It was used to diagnose medical parasites and arthropods with 95.0% accuracy. However, this technique still requires costly reagents and equipment. Geometric morphometric analysis is the statistical analysis of the patterns of shape change of an anatomical structure. Its accuracy is approximately 94.0–100.0%, and unlike DNA barcoding, costly reagents and equipment are not required. Artificial intelligence technology involves the analysis of pictures using well-trained algorithms. It showed 98.8–99.0% precision. All three approaches use computer programs instead of human interpretation. They also have the potential to be high-throughput technologies since many samples can be analyzed at once. However, the limitation of using these techniques in real settings is species coverage.

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Section: Advanced Approaches For Medical Parasite and Arthropod Diagnosesmentioning

confidence: 99%

State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

Ruenchit

2021

Diagnostics

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“…Usually medical validation of phenotypic resistance profiles are performed to check whether there are unusual resistances, which prompts additional testing or confirmation, e.g., detection of a potential extended spectrum beta-lactamase (ESBL) or carbapenemaseproducing bacteria due to suspicious MICs levels. This would trigger subsequent phenotypic or genotypic analysis [41,42]. Additional examples in the analytical step include the identification of bacterial and fungal species using matrix-assisted laser desorption/ ionization time of flight (MALDI-TOF) mass spectrometry.…”

Section: Opportunities For Digitalization In the Microbiology Diagnostic Processmentioning

confidence: 99%

“…Expert system Does the detected resistance profile make sense? e Medical validation of antibiotic resistance profiles with expert database [41,42] Public Health Is there a potential outbreak? e Automated screening for pathogen similarities, e.g., resistance profile or automated bioinformatics [130,131] Post-analytics Highlight important data Is there a potential bacterial phenotype?…”

Section: Opportunities For Digitalization In the Microbiology Diagnostic Processmentioning

confidence: 99%

Digital microbiology

Egli

Schrenzel

Greub³

2020

Clinical Microbiology and Infection

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Background: Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. Objective: This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process. Sources: We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology. Content: We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory. Implications: We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.

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“…The prepared slides were processed in the device and automatically analyzed via the MYLA software (bioMérieux, France). At the ending of the analysis, spectra were obtained (12).…”

Section: Development Of Antibody Biosensor and Biosensorbased Electro...mentioning

confidence: 99%

Rapid identification of Klebsiella pneumoniae isolates from various samples with biosensor and genotyping

Savaş¹

2019

Ankara Üniversitesi Veteriner Fakültesi Dergisi

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The most important cause of Klebsiella spp. contamination of drinking water is the leakage of animal faeces into drinking water sources. Recently, the biosensor technology has quickly begun to replace other methods with its faster finding and reliability. The aim of this study was to investigate the reliability of the biosensor technology in the rapid detection of Klebsiella pneumoniae (K. pneumoniae) and to determine the presence of the relationship between K. pneumoniae isolates isolated from the drinking water thought to be contaminated by animal faeces and the clinical isolates. For this purpose, portable, microfluidic electrochemical sensor device version 2 (V2) was used for the detection of K. pneumoniae and results were confirmed with VITEK MALDI-TOF Mass Spectrometry (VITEK MS) automated system. For the molecular typing of K. pneumoniae isolates, pulsed-field gel electrophoresis (PFGE) and multiple locus variable-number tandem repeat analysis (MLVA) methods were employed and the results were compared. For these bacteria, the most appropriate typing method was tried to be determined comparatively. PFGE analysis indicated the presence of six different strains, while MLVA divided them into 23 clusters. Clonal relationships were viewed between environmental and clinical isolates. The main goal of this paper is to present, the detailed report of the comparison of the samples isolated from drinking water, animal and human faeces for K. pneumoniae. To accomplish of this goal we introduced that MLVA and PFGE methods. Also, gold nanoparticies enhanced electrochemical biosensor device is used for the determination of K. pneumoniae for the first time.

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Characterization of Klebsiella isolates by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and determination of antimicrobial resistance with VITEK 2 advanced expert system (AES)

Cited by 7 publications

References 28 publications

State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

Digital microbiology

Rapid identification of Klebsiella pneumoniae isolates from various samples with biosensor and genotyping

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