Determinaçao das principais proteínas de fase aguda e do índice prognóstico inflamatório nutricional (IPIN) em cachorrodo-mato (Cerdocyon thous - Linnaeus, 1766)

The dog-eating fox (Cerdocyon thous - Linnaeus, 1766) is a medium sized canid widely distributed in South America and occurs in almost all of Brazil. Among the main threats to their conservation are the roadkill mainly caused by habitat loss. The shortage of laboratory bush dogs data affect the veterinary medical care hindering the application of appropriate therapies. This study aimed to evaluate the levels of C-reactive protein, albumin, pre-albumin, ceruloplasmin, haptoglobin and Afla 1 acid glycoprotein and the Prognostic Index Inflammatory Nutritional (IPIN) in this species, thus obtaining a first description of these prognostic markers. They collected 1.5 ml of blood by jugular access 8 of Mato Dogs copies (thous thous) from the Laboratory of collection of Teaching and Research in Wildlife (limpets), Faculty of Veterinary Medicine, Federal University of Uberlândia for exams routine. The samples were collected via the jugular vein after physical restraint of animals and trichotomy of the region. After statistical analysis, the values were: albumin: between 2.7 and 3.0 g / dl, alpha 1-acid glycoprotein: between 0.19 and 0.21 g / l, C-reactive protein: between 1.7 and 2 2, prealbumin between 30 and 35 mg / l haptoglobin: between 0.078 and 0.156 and IPIN ≤ 0.006 being considered normal and values ≥ 0.006 considered high. This press description will serve as a basis for studies where animals may be used with specific diseases and, after analysis, compared with the values found in this study and verified the behavior follows the likeness of domestic dogs.

Análise da expressão dos receptores HER-2 e sua correlação clínica em pacientes com neoplasia gástrica do setor de Oncologia do Hospital de Clínicas da Universidade Federal de Uberlândia-UFU

Introduction: Gastric cancer is currently the fourth higher cancer mortality rate among men in the world and the fifth among women, despite the progressive advances in oncology. The identification of tumor receptors and the development of target-drugs to block them has contributed to increased survival and quality of life of patients, but it becomes important to know the tumor profile of the population being treated, avoiding burdening treatment with examinations and treatments that are not cost-effective. Objective: To evaluate the profile of the population with gastric cancer treated in five years at the Clinical Hospital of the Federal University of Uberlândia and verify the correlation between overexpression of HER-2 receptor with an unfavorable prognosis. Methods: 203 records with gastric cancer were selected through the system database, attending a five-year period, of which 117 paraffin blocks were available for immunohistochemical assessment of HER2 receptor. Results: 2.6% of tumors showed overexpression of HER2, considering for this study two crosses as positive. There was no statistically significant difference in correlation between expression of the HER2 receptor with age, gender, tumor grade, local involvement, Lauren classification, Borrmann classification or staging. Conclusion: For this studied population, we can conclude that there is no need to employ HER2 blockers with high cost as a target-therapy in patients with gastric cancer, since no clinical benefit probably will be obtained due to a low percentage of these patients that demonstrated superexpression of this receptor or even there is no patients with gastric cancer with superexpression of HER2 with more than three crosses of positivity in immunochemistry

Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.