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Detection of Glasser's Disease in clinical samples using Polymerase chain reaction.


Citation

Teh, See Wai and Ooi, Peck Toung (2011) Detection of Glasser's Disease in clinical samples using Polymerase chain reaction. In: 6th Proceedings of the Seminar on Veterinary Sciences, 11-14 Jan. 2011, Kuala Lumpur . (pp. 99-103).

Abstract

Glasser’s Disease is one of the porcine common respiratory problems in out country. Glasser’s disease is caused by Haemophilus parasuis (HP). The HP has a wide range of antibiotics sensitivity and can be treated during early infection; therefore early diagnosis is very important. In the past, diagnosis was based on history, clinical signs and postmortem lesions, then confirm by bacteria isolation and identification. These traditional microbiology methods are time-consuming and labourious. Polymerase chain reaction (PCR) is one of the methods that provide rapid and accurate diagnosis. The procedure can be completed within 1 to 2 days and only require one trained personal to perform. Currently, there is limited publish articles on PCR test for the diagnosis of Glasser’s Disease in Malaysia. For the analysis 2 pairs of primers set for HP were selected; HPS1-forward (5’AGT AGG AAG GGT GGT GT 3’) and HPSI-reverse (5’ CGT TTC GTC ACC CTC TGT GT 3’) and HPS2- forward (5’ TAG AAA AAA TCT TTA ATT G 3’) and HPS2-reverse (5’ CAC CAT AGA AAC TTC TTT TC 3’). Lung tissues, pericardial swabs and thoracic swabs samples were collected form farms in Sepang, Selangor, Malaysia. The PCR test was carried after some modifications and optimization. Both HP primers able to detect positive clinical lung samples and can be further developed as PCR diagnosis tool in out country.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Veterinary Medicine
Keywords: Glasser's disease; Polymerase chain reaction.
Depositing User: Azizan Arshad
Date Deposited: 19 Nov 2013 03:36
Last Modified: 20 Dec 2013 05:09
URI: http://psasir.upm.edu.my/id/eprint/26716
Statistic Details: View Download Statistic

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