Importance of High-Risk Human Papillomavirus Infection Detection in Female Renal Transplant Recipients in the First Year after Transplatation

Abstract

OBJECTIVES: Most of human papilomavirus (HPV) infections are “cleared” by the immune system, however, in cases of immune system suppression infections could lead to development of malignancies. The aim of this study was to find out the frequency of HR-HPV infection in early period after renal transplantation in Latvian recipients receiving immunosuppressive therapy and to follow the progression of the infection up to one year. METHODS: 43 female renal recipients (median age of 48 IQR= 39-58) and 79 practically healthy female individuals (median age of 48 IQR= 42-57) as a control group were enrolled in this investigation. For the detection of HPV infection patients' samples (blood and vaginal swabs) where collected two weeks after transplantation with following collection of six months and one year. Different polymerase chain reactions for HR-HPV genomic sequences detection and commercial ELISA kit for detection of anti-HPV IgG antibodies were used. RESULTS: In this study we show that frequency rate of HR-HPV infection has increased by the one year after transplantation from early stage of immumosuppressive therapy (from 24% to 36%). Also increase of HR-HPV load was detected over the time, showing the highest median viral load at sixth month after transplantation. CONCLUSIONS: From the obtained data follows that it is very important to carefully monitor patients receiving immunosuppression therapy on progression of HR-HPV. In the case of this viral infection presence, immunosuppressive therapy must be attentively adjusted to avoid the HR-HPV infection rapid progression with the subsequent development of CIN or cervical cancer.

Mathematical model for the novel coronavirus (2019‐nCOV) with clinical data using fractional operator

“…Moreover, the fractional calculus results are of a more generic type [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Therefore, several fractional models have been used to investigate the spread of the novel COVID‐19 (2019‐nCOV) [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. They concluded that fractional models propagate faster than classical models and have useful scaling qualities that make them desirable for applications.…”