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Objective• To analyse the immunohistochemical and mRNA expression of SWI/SNF (SWItch/Sucrose NonFermentable) complex subunit polybromo-1 (PBRM1) in clear cell renal cell carcinoma (ccRCC) and its impact on clinical outcomes. Patients and Methods• In all, 213 consecutive patients treated surgically for renal cell carcinoma (RCC) between 1992 and 2009 were selected. • A single pathologist reviewed all cases to effect a uniform reclassification and determined the most representative tumour areas for construction of a tissue microarray.• In addition, mRNA expression of PBRM1 was analysed by reverse transcriptase-polymerase chain reaction. Results• Of the 112-immunostained ccRCC specimens, 34 (30.4%) were PBRM1-negative, and 78 (69.6%) were PBRM1-positive.• The protein expression of PBRM1 was associated with tumour stage (P < 0.001), clinical stage (P < 0.001), pN stage (P = 0.035) and tumour size (P = 0.002).• PBRM1 mRNA expression was associated with clinical stage (P = 0.023), perinephric fat invasion (P = 0.008) and lymphovascular invasion (P = 0.042).• PBRM1 significantly influenced tumour recurrence and tumour-related death. Disease-specific survival rates for patients whose specimens showed positive-and negative-PBRM1 expression were 89.7% and 70.6%, respectively (P = 0.017).• Recurrence-free survival rates in patients with positive-and negative-expression of PBRM1 were 87.3% and 66.7%, respectively (P = 0.048). Conclusions• PBRM1-negative expression is a markedly poor prognosis event in ccRCC.• We encourage PBRM1 study by other groups in order to validate our findings and confirm its possible role as a useful marker in the management of patients with ccRCC.

BackgroundLeishmaniasis remains among the most important parasitic diseases in the developing world and visceral leishmaniasis (VL) is the most fatal. The hamster Mesocricetus auratus is a susceptible model for the characterization of the disease, since infection of hamsters with L. infantum reproduces the clinical and pathological features of human VL. In this context, it provides a unique opportunity to study VL in its active form. The main goal of this study was to evaluate the clinical, biochemical, and hematological changes in male hamsters infected through different routes and strains of L. infantum.MethodsIn the current study, hamsters (Mesocricetus auratus) were infected with the L. infantum strains (WHO/MHOM/BR/74/PP75 and MCAN/BR/2008/OP46) by intradermal, intraperitoneal and intracardiac routes. The animals were monitored for a nine  month follow-up period.ResultsThe hamsters showed clinical signs similar to those observed in classical canine and human symptomatic VL, including splenomegaly, severe weight loss, anemia, and leucopenia. Therefore the OP46 strain was more infective, clinical signs were more frequent and more exacerbated in IC group with 80 to 100 % of the animals showing splenomegaly, in the last month infection. Additionally, desquamation, hair loss and external mucocutaneous lesions and ulcers localized in the snout, accompanied by swelling of the paws in all animals, were observed. Consequently, the animals presented severe weight loss/cachexia, hunched posture, an inability to eat or drink, and non-responsiveness to external stimuli. Furthermore, regardless of strain, route of inoculum and time assessed, the animals showed renal and hepatic alterations, with increased serum levels of urea and creatinine as well as elevated serum levels of aspartate aminotransferase and alanine aminotransferase.ConclusionsThese results strongly suggest that the inoculation through the intracardiac route resulted in a higher severity among infections, especially in the sixth and ninth month after infection via intracardiac, exhibited clinical manifestations and biochemical/hematological findings similar to human visceral leishmaniasis. Therefore, we suggest that this route must be preferentially used in experimental infections for pathogenesis studies of VL in the hamster model.

Background Cervical cancer progresses slowly, increasing the chance of early detection of pre‐neoplastic lesions via Pap exam test and subsequently preventing deaths. However, the exam presents both false‐negatives and false‐positives results. Therefore, automatic methods (AMs) of reading the Pap test have been used to improve the quality control of the exam. We performed a literature review to evaluate the feasibility of implementing AMs in laboratories. Methods This work reviewed scientific publications regarding automated cytology from the last 15 years. The terms used were “Papanicolaou test” and “Automated cytology screening” in Portuguese, English, and Spanish, in the three scientific databases (SCIELO, PUBMED, MEDLINE). Results Of the resulting 787 articles, 34 were selected for a complete review, including three AMs: ThinPrep Imaging System, FocalPoint GS Imaging System and CytoProcessor. In total, 1 317 148 cytopathological slides were evaluated automatically, with 1 308 028 (99.3%) liquid‐based cytology slides and 9120 (0.7%) conventional cytology smears. The AM diagnostic performances were statistically equal to or better than those of the manual method. AM use increased the detection of cellular abnormalities and reduced false‐negatives. The average sample rejection rate was ≤3.5%. Conclusion AMs are relevant in quality control during the analytical phase of cervical cancer screening. This technology eliminates slide‐handling steps and reduces the sample space, allowing professionals to focus on diagnostic interpretation while maintaining high‐level care, which can reduce false‐negatives. Further studies with conventional cytology are needed. The use of AM is still not so widespread in cytopathology laboratories.

In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.