RESUMO
BACKGROUND/AIM: During ovarian cancer (OC) debulking surgery, the surgeon can examine the peritoneal cavity for malignant cancer cells with peritoneal washing (PW) cytology. The goal of this study was to examine the significance of peritoneal washing as a prognostic indicator for ovarian cancer patients. PATIENTS AND METHODS: Information considering the prognostic factors of OC and their impact in PW's result was collected, compared, and combined. RESULTS: Omental metastasis, tumor type, tumor invasion, tumor size, tumor grade/ stage, tumor's cytoreduction, and recurrence affect both the peritoneal washing result and the patient's prognosis. The correlation that most of the above factors have with a positive PW and dismal prognosis, led us to the assumption that PW has a significance as a prognostic indicator. CONCLUSION: The significance of PW as a prognostic indicator remains an assumption.
RESUMO
Multiple omics data are rapidly becoming available, necessitating the use of new methods to integrate different technologies and interpret the results arising from multimodal assaying. The MathIOmica package for Mathematica provides one of the first extensive introductions to the use of the Wolfram Language to tackle such problems in bioinformatics. The package particularly addresses the necessity to integrate multiple omics information arising from dynamic profiling in a personalized medicine approach. It provides multiple tools to facilitate bioinformatics analysis, including importing data, annotating datasets, tracking missing values, normalizing data, clustering and visualizing the classification of data, carrying out annotation and enumeration of ontology memberships and pathway analysis. We anticipate MathIOmica to not only help in the creation of new bioinformatics tools, but also in promoting interdisciplinary investigations, particularly from researchers in mathematical, physical science and engineering fields transitioning into genomics, bioinformatics and omics data integration.
