HPV and vaginal microecological disorders in infertile women: a cross-sectional study in the Chinese population.

BACKGROUND: The purpose of this study was to evaluate the distributions of vaginal microbiome dysbiosis and human papillomavirus (HPV) subtypes in infertile women and explore the correlations of HPV infection and vaginal microbiome dysbiosis with infertility. METHODS: In total, 1464 women aged 18-50 years were included in this study; 649 participants were included in the infertility group, and 815 participants were included in the normal group. The participants were tested for HPV, and their vaginal microecology was examined. The χ2 test and Spearman regression were used for statistical analysis, and binary logistic regression was performed to identify the risk factors for infertility. RESULTS: The patients in the infertility group were younger than those in the normal group, and the proportions of bacterial vaginosis and vaginal imbalance in the infertility group were significantly higher than those in the normal group. The incidence proportions of high-risk HPV types in the infertility group were significantly higher than those in the normal group, and the proportions of high-risk subtytes HPV16, HPV39, HV52, HPV56, and HPV68 were significantly higher in the infertility group than in the normal group. However, there were no significant differences in the incidences of low-risk HPV types. The incidence proportions of vaginal flora imbalance and HPV infection in the infertility group were significantly higher than those in the normal group. HPV16, HPV33, HPV51, HPV52and HPV58 infections were independent risk factors for infertility. CONCLUSIONS: Vaginal microecological imbalance and HPV infection are directly related to infertility, and precautions should be taken.

Development of Models to Predict Postoperative Complications for Hepatitis B Virus-Related Hepatocellular Carcinoma.

BACKGROUND: Surgical treatment remains the best option for patients with hepatocellular carcinoma (HCC) caused by chronic hepatitis B virus (HBV) infection. However, there is no optimal tool based on readily accessible clinical parameters to predict postoperative complications. Herein, our study aimed to develop models that permitted risk of severe complications to be assessed before and after liver resection based on conventional variables. METHODS: A total of 1,047 patients treated by hepatectomy for HCC with HBV infection at three different centers were recruited retrospectively between July 1, 2014, and July 1, 2018. All surgical complications were recorded and scored by the Comprehensive Complication Index (CCI). A CCI ≥26.2 was used as a threshold to define patients with severe complications. We built two models for the CCI, one using preoperative and one using preoperative and postoperative data. Besides, CCI and other potentially relevant factors were evaluated for their ability to predict early recurrence and metastasis. All the findings were internally validated in the Hangzhou cohort and then externally validated in the Lanzhou and Urumqi cohorts. RESULTS: Multivariable analysis identified National Nosocomial Infections Surveillance (NNIS) index, tumor number, gamma-glutamyltransferase (GGT), total cholesterol (TC), potassium, and thrombin time as the key preoperative parameters related to perioperative complications. The nomogram based on the preoperative model [preoperative CCI After Surgery for Liver tumor (CCIASL-pre)] showed good discriminatory performance internally and externally. A more accurate model [postoperative CCI After Surgery for Liver tumor (CCIASL-post)] was established, combined with the other four postoperative predictors including leukocyte count, basophil count, erythrocyte count, and total bilirubin level. No significant association was observed between CCI and long-term complications. CONCLUSION: Based on the widely available clinical data, statistical models were established to predict the complications after hepatectomy in patients with HBV infection. All the findings were extensively validated and shown to be applicable nationwide. Such models could be used as guidelines for surveillance follow-up and the design of post-resection adjuvant therapy.