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Genetic detection to Aeromonas hydrophila proteolytic activity in milk samples (cows, buffaloes and goats) in Basra governorate

    Author

    Department of Pharmacy, Basra Technical Institute, Southern Technical University, Basra, Iraq

,

Document Type : Research Paper

Abstract

This study aim is to determine the incidence and the virulence of Aeromonas hydrophila in raw milk, randomly collected from Basra governorate by using of polymerase chain reaction (PCR) technique. In this study, the total number of raw milk samples collected from cows, buffaloes and goats that kept from different the regions of Basra governorate were 90 samples. The PCR technique is modern method which regarded as a reliable tool to detect virulent gene of the A. hydrophila isolates. The PCR assays using the primers sets SerAh-F and SerAh-R resulted in the amplification of 650-bp bands from the targeted proteases gene of the A. hydrophil. The result of the present study showed that the results of PCR concerning the proteolytic activity of A. hydrophila in the tested raw milk samples according to animals' source. The higher percentage of the proteolytic activity was found in the cow's raw milk samples 40% and in the buffalo's milk samples was 26.7% while, the proteolytic activity did not find in the goat's milk samples. The association between the source of the milk sample and proteolytic A. hydrophila positive results was considered to be statistically highly significant. The higher percentage of the A. hydrophila isolates found in the raw cow milk was 40%, and the A. hydrophila isolates found in the raw buffalo milk was 26.7%, while, the A. hydrophila isolates did not find in the goat milk.

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Highlights

  1. A novel hybrid structure technique is used to control a large-scale nonlinear system
  2. FHLC talent to deal with MIMO plants and can potentially lead to 15.5% energy savings
  3. Converting Takagi-Sugeno fuzzy inference system into faster hybrid layers structure
  4. FHLC achieved precision and robustness due to online tuning by using gradient algorithm
  5. FHLC can be deemed as one of the most important components of intelligent buildings

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Iraqi Journal of Veterinary Sciences
Volume 34, Issue 2
July 2020
Page 253-258
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History
  • Received: 11 July 2019
  • Revised: 21 August 2019
  • Accepted: 04 September 2019
  • Published: 01 July 2020
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