Next Article in Journal
Dynamic Denoising and Gappy Data Reconstruction Based on Dynamic Mode Decomposition and Discrete Cosine Transform
Previous Article in Journal
The Effect of Functional Group Polarity in Palladium Immobilized Multiwalled Carbon Nanotube Catalysis: Application in Carbon–Carbon Coupling Reaction
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(9), 1512; https://doi.org/10.3390/app8091512

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
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
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)
Full-Text   |   PDF [1783 KB, uploaded 1 September 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top