[TAp73α is Upregulated in the Most Common Human Cancers].

The transcription factor p73 is a member of the p53 tumor suppressor gene family and one of the key regulators of apoptosis. TP73 gene encodes two protein isoforms classes with diverse functions, TAp73 and DNp73, and TAp73 expression in tumor tissues is altered. Unlike the TP53 gene, TP73 is not mutated in cancers. Here, we sought to explore the expression of p73 isoforms across eight major cancer types using the publicly available data deposited at the GDC data portal and the TSVdb database. Our results showed that TAp73α is overexpressed in breast invasive carcinoma, stomach adenocarcinoma, lung squamous cell carcinoma, colon adenocarcinoma, and esophageal carcinoma tumors, whereas the expression of DNp73 isoforms is downregulated in breast invasive carcinoma (DNp73α,ß,γ), Prostate Adenocarcinoma (DNp73ß), Lung Adenocarcinoma (DNp73α), Lung Squamous Cell Carcinoma (DNp73α) tumors. In summary, this study revealed that TAp73α has higher expression than the DNp73 isoforms in several cancer types.

sandflyDST: a dynamic web-based decision support tool for the morphological identification of sandflies present in Anatolia and mainland Europe, and user study.

Species identification of sandflies is mainly performed according to morphological characters using classical written identification keys. This study introduces a new web-based decision support tool (sandflyDST) for guiding the morphological identification of sandfly species present in Anatolia and mainland Europe and classified in the Phlebotomus and Sergentomyia genera (both: Diptera: Psychodidae). The current version of the tool consists of 111 questions and 36 drawings obtained from classical written keys, and 107 photographs for the quick and easy identification of 26 species of the genus Phlebotomus and four species of the genus Sergentomyia. The tool guides users through a decision tree using yes/no questions about the morphological characters of the specimen. The tool was applied by 30 individuals, who then completed study questionnaires. The results of subsequent analyses indicated that the usability (x‾SUSScore=75.4) and users' level of appreciation (86.6%) of the tool were quite high; almost all of the participants considered recommending the tool to others. The tool may also be useful in training new entomologists and maintaining their level of expertise. This is a dynamic tool and can be improved or upgraded according to feedback. The tool is now available online at http://parasitology.ege.edu.tr/sandflyDST/index.php.

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree.

BACKGROUND: The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making. OBJECTIVE: The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. METHODS: The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. RESULTS: In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. CONCLUSIONS: The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.