Types and viral load of human papillomavirus, and vaginal microbiota in vaginal intraepithelial neoplasia: a cross-sectional study.

BACKGROUND: Human papilloma virus (HPV) infection is an important risk factor for vaginal intraepithelial neoplasia (VAIN). Recent studies have suggested that the microbiome may play a potential role in cervicovaginal diseases. This study aimed to explore the characteristics of the types and viral load of HPV in VAIN, as well as the association between vaginal microbiota and VAIN. METHODS: A total of 176 women, either with VAIN, or without VAIN but with HPV infection were enrolled in the study. Among them, 109 HPV positive cases were qualified for viral load assay. The vaginal microbiota of 122 HPV positive women, who were matched by severity of cervical lesions and menopause status, was determined by 16S ribosomal RNA (16S rRNA) sequencing. RESULTS: The top 5 types of HPV-associated vaginal lesions were HPV16 (24.2%), HPV52 (24.2%), HPV53 (16.1%), HPV58 (14.5%) and HPV66 (14.5%). The viral load of HPV types 16, 52, and 58 appeared higher in separate vaginal lesions than in histopathologically normal cases (P=0.026, 0.002, and 0.013, respectively). The vaginal microbiota of HPV-positive patients with VAIN did not exhibit a large change in diversity. Vaginal microbiota of VAIN was characterized by an increased abundance of Atopobium, Gardnerella, Allobaculum and Clostridium, as well as decreased abundance of Finegoldia, Actinobaculum and Blautia. A higher level of Enterococcus and some specific Clostridium spp. might be associated with an elevated risk of VAIN2/3. CONCLUSIONS: A higher level of viral load of HPV16, 52, and 58 may indicate VAIN. The composition of vaginal microbiota changes during the progression of VAIN and specific bacteria such as Atopobium, Gardnerella, Allobaculum, Enterococcus and Clostridium, may help to promote its development.

Robustified maximum likelihood estimation in generalized partial linear mixed model for longitudinal data.

In this article, we study the robust estimation of both mean and variance components in generalized partial linear mixed models based on the construction of robustified likelihood function. Under some regularity conditions, the asymptotic properties of the proposed robust estimators are shown. Some simulations are carried out to investigate the performance of the proposed robust estimators. Just as expected, the proposed robust estimators perform better than those resulting from robust estimating equations involving conditional expectation like Sinha (2004, Journal of the American Statistical Association99, 451-460) and Qin and Zhu (2007, Journal of Multivariate Analysis98, 1658-1683). In the end, the proposed robust method is illustrated by the analysis of a real data set.