The diagnosis of patients with malignancies relies on the results of a clinical cytological examination. To enhance the diagnostic qualities of cytological examinations, it is important to have a detailed analysis of the cell's characteristics. There is, therefore, a need for developing a new auxiliary method for cytological diagnosis. In this study, we focused on studying the charge of the cell membrane surface of fixed cells, which is one of important cell's characteristics. Although fixed cells lose membrane potential which is observed in living cells owing to ion dynamics, we hypothesized that fixed cells still have a cell membrane surface charge due to cell membrane components and structure. We used 5 cell lines in this study (ARO, C32TG, RT4, TK, UM-UC-14). After fixation with CytoRich Red, we measured the cell membrane surface charge of fixed cells in solution using zeta potential measurements and fixed cells on glass slides, visualizing it using antibody-labeled beads and positivelycharged beads. Furthermore, we measured the cell membrane surface charge of fixed cells under different conditions, such as different solution of fixative, ion concentration, pH, and pepsin treatments. The zeta potential measurements and visualization using the beads indicated that the cell membrane surface of fixed cells was negatively charged, and also that the charge varied among fixed cells. The charge state was affected by the different treatments. Moreover, the number of cell-bound beads was small in interphase, anaphase, and apoptotic cells. We concluded that the negative cell membrane surface charge was influenced by the three-dimensional structure of proteins as well as the different types of amino acids and lipids on the cell membrane. Thus, cell surface charge visualization can be applied as a new auxiliary method for clinical cytological diagnosis. This is the first systematic report of the cell membrane surface charge of fixed cells.
Background Human papillomavirus (HPV) is a well‐established mucosotropic carcinogen, but its impact on urothelial neoplasm is unclear. We aimed to clarify the clinical and pathological features of HPV‐related urothelial carcinoma (UC). Methods Tissue samples of 228 cases of UC were obtained from the bladder, upper and lower urinary tract, and metastatic sites to construct a tissue microarray. The samples were analyzed for the presence of HPV by a highly sensitive and specific mRNA in situ hybridization (RISH) technique (RNAscope) with a probe that can detect 18 varieties of high‐risk HPV. We also conducted immunohistochemistry (IHC) for a major HPV capsid antibody and DNA‐PCR. Results The HPV detection rates varied among the methods; probably due to low HPV copy numbers in UC tissues and the insufficient specificity and sensitivity of the IHC and PCR assays. The RISH method had the highest accuracy and identified HPV infection in 12 (5.2%) of the cases. The histopathological analysis of the HPV‐positive UC showed six cases of usual type UC, five cases of UC with squamous differentiation (UC_SqD), and one case of micropapillary UC. The HPV detection rate was six‐fold higher in the cases of UC_SqD than in the other variants of UC (odds ratio [OR] =8.9, p = 0.002). In addition, HPV infection showed a significant association with tumor grade (OR =9.8, p = 0.03) and stage (OR =4.7, p = 0.03) of UC. Moreover, the metastatic rate was higher in HPV‐positive than in negative UC (OR =3.4). Conclusion These data indicate that although the incidence of HPV infection in UC is low, it is significantly associated with squamous differentiation and poor prognosis. Furthermore, our observations show that RNAscope is an ideal method for HPV detection in UC compared with the other standard approaches such as IHC and PCR assays.
Objectives Pathologic diagnosis of flat urothelial lesions is subject to high interobserver variability. We expected that deep learning could improve the accuracy and consistency of such pathologic diagnosis, although the learning process is a black box. We therefore propose a new approach for pathologic image classification incorporating the diagnostic process of the pathologist into a deep learning method. Methods A total of 267 H&E-stained slides of normal urothelium and urothelial lesions from 127 cases were examined. Six independent convolutional neural networks were trained to classify pathologic images according to six pathologic criteria. We then used these networks in the main training for the final diagnosis. Results Compared with conventional manual analysis, our method significantly improved the classification accuracy of images of flat urothelial lesions. The automated classification showed almost perfect agreement (weighted κ = 0.98) with the consensus reading. In addition, our approach provides the advantages of reliable diagnosis corresponding to histologic interpretation. Conclusions We used deep learning to establish an automated subtype classifier for flat urothelial lesions that successfully combines traditional morphologic approaches and complex deep learning to achieve a learning mechanism that seems plausible to the pathologist.
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