Rationale: About 8384 cases of solid pseudopapillary neoplasms (SPN) of pancreas have been published in English literature, from 1933 to 2018. This is a low-grade tumor that usually occurs in children but is rare in adults and, in exceptional cases, can show extrapancreatic localization. In this paper we present 2 unusual cases of SPNs, 1 with retroperitoneal location (case 1) and 1 that was firstly diagnosed as a G1 neuroendocrine tumor (NET) and showed hepatic metastases after 13 years (case 2).
View Article and Find Full Text PDFThe effect of various toxicants on growth/death and morphology of human cells is investigated using the xCELLigence Real-Time Cell Analysis High Troughput in vitro assay. The cell index is measured as a proxy for the number of cells, and for each test substance in each cell line, time-dependent concentration response curves (TCRCs) are generated. In this paper we propose a mathematical model to study the effect of toxicants with various initial concentrations on the cell index.
View Article and Find Full Text PDFThe occurrence of a large number of diverse arsenic species in the environment and in biological systems makes it important to compare their relative toxicity. The toxicity of arsenic species has been examined in various cell lines using different assays, making comparison difficult. We report real-time cell sensing of two human cell lines to examine the cytotoxicity of fourteen arsenic species: arsenite (As), monomethylarsonous acid (MMA) originating from the oxide and iodide forms, dimethylarsinous acid (DMA), dimethylarsinic glutathione (DMAG), phenylarsine oxide (PAO), arsenate (As), monomethylarsonic acid (MMA), dimethylarsinic acid (DMA), monomethyltrithioarsonate (MMTTA), dimethylmonothioarsinate (DMMTA), dimethyldithioarsinate (DMDTA), 3-nitro-4-hydroxyphenylarsonic acid (Roxarsone, Rox), and 4-aminobenzenearsenic acid (p-arsanilic acid, p-ASA).
View Article and Find Full Text PDFBackground: Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances.
Results: In this paper, we present machine learning approaches for MOA assessment.