ISSN: 0973-7510

E-ISSN: 2581-690X

Research Article | Open Access
Ayman Elbehiry1,2 , Musaad Al-Dubaib3, Eman Marzouk1, Fahd Mohammad Albejaidi4, Mohamed A.A. Radwan5,6, Feras Alzaben3,7 and Ahmad Alharbi3,8
1Department of Public Health, College of Public Health and Health Informatics, Qassim University, Buraidah, Saudi Arabia.
2Department of Bacteriology, Mycology and Immunology, Faculty of Veterinary Medicine, University of Sadat City, Sadat City, Egypt.
3Department of Veterinary Medicine, College of Agriculture and Veterinary Medicine, Qassim University, Buraidah, Saudi Arabia.
4Department of Health Administration, College of Public Health and Health Informatics, Qassim University, Kingdom of Saudi Arabia.
5Department of Chemistry, Faculty of Science, Qassim University, Buraidah, Saudi Arabia.
6Applied Organic Chemistry Department, National Research Centre, Dokki, Giza, Egypt.
7Department of Preventive Medicine, King Fahad Armed Forces Hospital, Jeddah City, Saudi Arabia.
8Ministry of Environment Water & Agriculture, Saudi Arabia.
J Pure Appl Microbiol, 2019, 13 (2): 1041-1052 | Article Number: 5574
Received: 18/04/2019| Accepted: 20/05/2019 | Published: 18/06/2019
Abstract

There are various sources of microbial air pollution which are seems to be a serious public health problem all over the world. For prevention and control of air pollution caused by airborne bacteria, rapid, sensitive and reliable detection techniques are required. Therefore, our study focused on using MALDI Biotyper (MBT) for rapid recognition of various microbial air pollutants. Five hundred air samples were collected from three localities, including Qassim University (150 samples), Al-Qassim hospitals (250 samples) and poultry slaughter houses (100 samples). All air samples were collected by impactor air sampler from the indoor and outdoor environment. All samples were cultivated on nutrient and blood agar media for two days and a total of 129 isolates were purified for proteomic analysis using MALDI Biotyper (MBT) then confirmed by quantitative polymerase chain reaction (qPCR). One hundred and nineteen (92.25%) isolates were identified by MBT at the species level with a log (score) value £2. 000 whereas; 10 (7.75%) isolates were detected at the genus level with score values ranged from 1.7000 to 1.999. The MBT was able to identify 93 (72.10%) gram-positive and 36 (27.90%) gram-negative bacterial isolates. The most common genera were Staphylococcus (n = 43, 33.33%), Escherichia (n = 16, 12.40%), Enterococcus (n = 15, 11.63%) and Bacillus (n = 15, 11.63%). Staphylococcus aureus and Escherichia coli were the most frequently identified species (n = 16, 12.40% for each). In general, we detected 53 (41.10%) various bacterial species in Al-Qassim Hospitals, 41 (31.79%) in poultry slaughter houses and 35 (27.13%) in Qassim University. Throughout Al-Qassim region, the air was tainted by numerous environmental microorganisms, and the MBT was positively adjusted for their fast and accurate identification.

Keywords

Airborne bacteria; MBT; Al-Qassim region; Saudi Arabia.

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© The Author(s) 2019. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.