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The Molecular Detection of HPV Infection in samples of Iraqi Women with Abnormal cervical Smears
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Human papillomavirus (HPV) types 16 and 18 cause almost 70% of cervical cancer cases worldwide. Recently, testing for high-risk HPV types have been adopted by clinical practices for the early detection of cervical cancer in conjunction with cytology tests.

Cervical swab samples were collected at the Outpatient Gynecology department of Baghdad Teaching Hospital. These samples consisted of a patient group of 50 samples, and a healthy control group of 10 samples. A papanicolaou test (abbreviated as a Pap test) was also performed for each woman to examine the epithelial cells of both the endocervix and the upper vaginal region. Total DNA (genomic, mitochondrial, and viral) was extracted from cervical swab samples for molecular studies.

HPV DNA testing was first done by using Real-Time PCR technology to target the L1 region of HR-HPV with specifically designed primers. This was followed by using AmpliSens kit for specific detection and genotyping of HPV16 and 18 with multiplex Real-Time PCR.

The results of RT-PCR detection revealed that out of 16 samples detected with high-risk HPV, 5 samples were shown to be infected with HPV-16 and 5 samples were shown to be infected with HPV-18. These results show a significant relationship between the histological outcome of the patient and persistent HPV infection.

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Publication Date
Fri Apr 15 2016
Journal Name
International Journal Of Computer Applications
Hybrid Techniques based Speech Recognition
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Information processing has an important application which is speech recognition. In this paper, a two hybrid techniques have been presented. The first one is a 3-level hybrid of Stationary Wavelet Transform (S) and Discrete Wavelet Transform (W) and the second one is a 3-level hybrid of Discrete Wavelet Transform (W) and Multi-wavelet Transforms (M). To choose the best 3-level hybrid in each technique, a comparison according to five factors has been implemented and the best results are WWS, WWW, and MWM. Speech recognition is performed on WWS, WWW, and MWM using Euclidean distance (Ecl) and Dynamic Time Warping (DTW). The match performance is (98%) using DTW in MWM, while in the WWS and WWW are (74%) and (78%) respectively, but when using (

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