In a population with a high prevalence of Ureaplasma spp, there was an association of this pathogen with high-risk HPV infection, a finding that needs further elucidation.
Background: Human Papilloma Virus has been considered as the main cause for cervical cancer. In this study we investigated epigenetic changes and especially methylation of specific sites of HPV genome. The main goal was to correlate methylation status with histological grade as well as to determine its accuracy in predicting the disease severity by establishing optimum methylation cutoffs.Methods: In total, sections from 145 cases genotyped as HPV16 were obtained from formalin- fixed, paraffin-embedded tissue of cervical biopsies, conization or hysterectomy specimens. Highly accurate pyrosequencing of bisulfite converted DNA, was used to quantify the methylation percentages of UTR promoter, enhancer and 5' UTR, E6 CpGs 494, 502, 506 and E7 CpGs 765, 780, 790. The samples were separated in different groupings based on the histological outcome. Statistical analysis was performed by SAS 9.4 for Windows and methylation cutoffs were identified by MATLAB programming language.Results: The most important methylation sites were at the enhancer and especially UTR 7535 and 7553 sites. Specifically for CIN3+ (i.e. HSIL or SCC) discrimination, a balanced sensitivity vs. specificity (68.1%, 66.2% respectively) with positive predictive value (PPV) and negative predictive value (NPV) (66.2%, 68.2% respectively) was achieved for UTR 7535 methylation of 6.1% cutoff with overall accuracy 67.1%, while for UTR 7553 a sensitivity 60.9%, specificity 69.0%, PPV=65.6%, NPV=64.5% and overall accuracy=65.0% at threshold 10.1% was observed.Conclusion: Viral HPV16 genome was found methylated in NF-1 binding sites of UTR in cases with high grade disease. Methylation percentages of E6 and E7 CpG sites were elevated at the cancer group.
Aim of this article is to investigate the potential of Artificial Intelligence (AI) in the discrimination between benign and malignant endometrial nuclei and lesions. For this purpose, 416 histologically confirmed liquid-based cytological smears were collected and morphometric characteristics of cell nuclei were measured via image analysis. Then, 50% of the cases were used to train an AI system, specifically a learning vector quantization (LVQ) neural network. As a result, cell nuclei were classified as benign or malignant. Data from the remaining 50% of the cases were used to evaluate the AI system performance. By nucleic classification, an algorithm for the classification of individual patients was constructed, and performance indices on patient classification were calculated. The sensitivity for the classification of nuclei was 77.95%, and the specificity was 73.93%. For the classification of individual patients, the sensitivity was 90.70% and the specificity 82.79%. These results indicate that an AI system can have an important role in endometrial lesions classification.
Numerous ancillary techniques detecting HPV DNA or mRNA are viewed as competitors or ancillary techniques to test Papanicolaou. However, no technique is perfect because sensitivity increases at the cost of specificity. Various methods have been applied to resolve this issue by using many examination results, such as classification and regression trees and supervised artificial neural networks. In this article, 1258 cases with results from test Pap, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of the self-organizing map (SOM). An artificial neural network has three advantages: it is unsupervised, can tolerate missing data, and produces topographical maps. The results of the SOM application were encouraging and produced maps depicting the important tests.
Ovarian cancer peritoneal carcinomatosis requires a multimodal-treatment approach. Current treatment considerations are analyzed in this update and include the management of recurrent malignant ascites and the understanding of its pathophysiology, the role of peritoneal washing cytology in detecting peritoneal metastases, capsular invasion and ovarian cancer histologic type, interpretation of pretreatment Ca-125 levels at different time points of ovarian cancer therapeutic management, characteristics of 10-year survivors of high-grade ovarian cancer, and the role of lymphadenectomy in ovarian cancer peritoneal carcinomatosis. This update also includes current considerations on the role of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in ovarian cancer peritoneal carcinomatosis as well as relevant ongoing phase III randomized controlled trial protocols.
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