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Volume 2, 2017, Issue 1, Pages 16-17; Paper doi: 10.15412/J.mnb.05020103; Paper ID: 30022.
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Intelligence Antibiogram Test for Targeted Antibiotic Use to Prevent Microbial Resistance
(Editorial)
  • 1 Department of Paramedical Sciences, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran
  • 2 Young Researchers and Elite Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
  • Correspondence should be addressed to Ghasem Rahimi, Young Researchers and Elite Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran; Tel: ; Fax: ; Email: rahimighasem75@yahoo.com.

Abstract

The misuse of antibiotics and unknown factors involved in the antibiotic resistance will result in resistance of microorganisms to antibiotics and will impose excessive costs on the governments for dealing with this phe-nomenon. Efforts for achieving intelligence diagnostic tools and increasing precision of the tools are very im-portant for accurate diagnosis of infections. Therefore, targeted antibiotic use is one practical strategy to reduce the antibiotic resistance.

Keywords

Antibiotic resistance, Antibiogram, Intelligence, Targeting

I

n the beginning of the twentieth century, the discovery of antibiotics has considerably helped the medical community, and this era is considered as a turning point in the fight against infectious microorganisms. Appearance of Intelligent behavior by these organisms in parallel with the use of antibiotics makes them resistant to the antibiotics (1). Rapidly increasing prevalence of resistance among microorganisms versus researchers' efforts to discover new antibiotics can be likened to a marathon, in which the microorganisms become adapted and resistant against the antibiotics. This problem is not specific to any particular region or country (2). Actually, it is a global phenomenon so that if it is not controlled, antibiotics will no longer be considered as an appropriate option for treatment of infectious diseases, and eventually will become a serious problem in the medical world. Undoubtedly, it can be said that without proper control and management of antibiotics use, the medical community will return to the pre- antibiotic era. The difference is that these organisms will be more intelligent and more resistant to the antibiotics so that the mildest infections will become the deadliest infections. The accurate identification of the factors involved in the resistance of microorganisms to the antibiotics will help in adoption of a timely and appropriate strategy for controlling this phenomenon. The mutation and gene transfer between bacteria, the synthesis of antibiotic-inactivating enzymes, inappropriate prescribing of antibiotics by physicians, self-medication with antibiotics, and failure to complete the course of antibiotic therapy, are among the factors contributing significantly to the antibiotic resistance phenomenon (3, 4). But one factor that has been less considered is human error in the antibiogram tests, particularly when it is done by non-specialists. Generally, an antibiogram test is performed based on the Kirby-Bauer method, in which the susceptibility of a microorganism is measured and reported according to the growth inhibition zone in millimeters. The growth inhibition zone is formed against antibiotics and compared with standard tables that are defined based on the Clinical and Laboratory Standards Institute (CLSI) (5). The factors such as inaccurate reading of an antibiotic, reading a test without measuring the growth inhibition zone or lack of precision in the measurement of the growth inhibition zone, or even imprecision in recording the reports, make those antibiotics no longer work efficiently. Additionally, these factors may result in ineffective use of antibiotics and bacterial resistance against the antibiotics. A precise diagnostic tool in the laboratories that is capable of performing an intelligent antibiogram test, analyzing its results compared to the standard protocols, detecting effective antibiotics, and determining the dose and duration of antibiotic treatment without human intervention, in turn, can contribute to reduce the antibiotic-resistance phenomenon. Furthermore, establishment of an integrated antibiotic-use management system and construction of a database based on the results of intelligent antibiogram tests will provide comprehensive and accurate information for World Health Organization (WHO) or health department of any country on the prevalence of infections and antibiotic susceptibility or resistance pattern, even from the most remote region of the world or a country, by which it will be possible to prevent the emergence of antibiotic resistance by adopting a timely and appropriate strategy. This program requires commitment of all laboratories to use an intelligent diagnostic antibiogram tool and uploading the results from the test (automatically) to the integrated antibiotic-use management system. The program will provide useful information, such as the status and the prevalence of an infection from the closest to the most remote region of a particular geographic area, the pattern of susceptibility or resistance to the antibiotics, self-medication with antibiotics, detection of ineffective antibiotics and their presentation to the community. Consequently, World Health Organization (WHO) or health departments of any country can establish an accurate, coherent and more targeted plan than before to prevent the antibiotic resistance.

Not mentioned any funding/ support by authors.

ACKNOWLEDGMENT

Not mentioned any acknowledgment by authors.

AUTHORS CONTRIBUTION

This work was carried out in collaboration among all authors.

CONFLICT OF INTEREST

The authors declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

REFERENCES

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Paper Title: Intelligence Antibiogram Test for Targeted Antibiotic Use to Prevent Microbial Resistance
Paper Details: Volume 2, Issue 1, Pages: 16-17
Paper doi:10.15412/J.mnb.05020103
Micro & Nano Biomedicine
Journal home page: http://journals.lexispublisher.com/mnb
Copyright © 2017 Alireza Khodavandi et al. This is an open access paper distributed under the Creative Commons Attribution License.
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