Urinary human papillomavirus DNA detection using piezoelectric biosensor

Cervical cancer is a disease that remains a concern for women worldwide. Despite the implementation of standard Pap test and the HPV test, the screening coverage is still low due to its invasive nature as both involve the collection of cervical samples. The HPV screening test itself is expensive, extensive and label-dependent. In this pilot study, piezoelectric biosensor was used for HPV DNA detection in urine due to its non-invasive approach, simplicity, low instrumentation costs, and label-free detection. Urine samples were collected from 21 women with abnormal Pap Test results and 19 women with normal Pap Test results. HPV HR piezoelectric biosensor was developed for the detection of 3 high-risk HPV DNA strains (16, 18, and 33) and HPV 16 piezoelectric biosensor is for the detection of only HPV 16 DNA. Probe optimisation and calibration experiments were carried out. Amplified urinary DNA samples were analysed using the biosensors. Results showed that the optimum probe concentration for both biosensors was 1.0 μM. The biosensor was able to detect the presence of complementary target DNAs with high specificity. For HPV HR piezoelectric biosensor, the sensitivity was 97.99 Hz μM -1, the instrument detection limit was 16.36 Hz and the concentration detection limit was 0.10344 μM. Meanwhile, for HPV 16 DNA piezoelectric biosensor, the sensitivity was 99.19 Hz μM -1, the instrument detection limit was 15.14 Hz and the concentration detection limit was 0.088 μM. The clinical sensitivity and specificity for both types of piezoelectric biosensor were both 100%. These preliminary results allow for the possibility of implementing the piezoelectric biosensor for the detection of urinary HPV DNA as a potential alternative screening method

Human Papillomavirus Risk Type Classification from Protein Sequences Using Support Vector Machines

Abstract. Infection by the human papillomavirus (HPV) is associated with the development of cervical cancer. HPV can be classified to highand low-risk type according to its malignant potential, and detection of the risk type is important to understand the mechanisms and diagnose potential patients. In this paper, we classify the HPV protein sequences by support vector machines. A string kernel is introduced to discriminate HPV protein sequences. The kernel emphasizes amino acids pairs with a distance. In the experiments, our approach is compared with previous methods in accuracy and F1-score, and it has showed better performance. Also, the prediction results for unknown HPV types are presented.