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Open AccessArticle

An Automatic Analysis System for High-Throughput Clostridium Difficile Toxin Activity Screening

1
Cancer Biology Program, Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
2
Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krakow, Poland
3
Signal Processing Systems Department, Faculty of Electronics, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
4
Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
5
Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
6
Faculty of Chemistry, Division of Medicinal Chemistry and Microbiology, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2018, 8(9), 1512; https://doi.org/10.3390/app8091512
Received: 31 July 2018 / Revised: 10 August 2018 / Accepted: 22 August 2018 / Published: 1 September 2018
(This article belongs to the Section Applied Biosciences and Bioengineering)
Clostridium difficile infection (CDI) is an increasing global health threat and major worldwide cause of hospital-acquired diarrhea. The development of novel therapies to effectively treat this bacterial pathogen is an unmet clinical need. Here, we describe an image processing and classification algorithm that automatically identifies toxin-induced cytotoxicity to host cells based on characteristic morphological changes. This efficient and automatic algorithm can be incorporated into a screening platform to identify novel anti-toxin inhibitors of the C. difficile major virulence factors TcdA and TcdB, and contains the following steps: image enhancement, cell segmentation, and classification. We tested the algorithm on 504 images (containing 5096 cells) and achieved 93% sensitivity and 91% specificity, indicating that the proposed computational approach correctly classified most of the cells and provided reliable information for an effective screening platform. This algorithm achieved higher classification results compared to existing cell counter and analysis programs, scoring 92.6% accuracy. Compared to visual examination by a researcher, the algorithm significantly decreased classification time and identified toxin-induced cytotoxicity in an unbiased manner. Availability: Examples are available at home.agh.edu.pl/jaworek/CDI. View Full-Text
Keywords: Clostridium difficile; image analysis; pattern recognition; classification; screening system; anti-toxin inhibitors Clostridium difficile; image analysis; pattern recognition; classification; screening system; anti-toxin inhibitors
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Garland, M.; Jaworek-Korjakowska, J.; Libal, U.; Bogyo, M.; Sieńczyk, M. An Automatic Analysis System for High-Throughput Clostridium Difficile Toxin Activity Screening. Appl. Sci. 2018, 8, 1512.

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