Universidade de Pernambuco

About: Universidade de Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Medicine. The organization has 6147 authors who have published 6948 publications receiving 73648 citations.

ATP inhibits insulin-degrading enzyme activity.

Abstract: We studied the ability of ATP to inhibit in vitro the degrading activity of insulin-degrading enzyme. The enzyme was purified from rat skeletal muscle by successive chromatographic steps. The last purification step showed two bands at 110 and 60 kDa in polyacrylamide gel. The enzyme was characterized by its insulin degradation activity, the substrate competition of unlabeled to labeled insulin, the profile of enzyme inhibitors, and the recognition by a specific antibody. One to 5 mM ATP induced a dose-dependent inhibition of insulin degradation (determined by trichloroacetic acid precipitation and insulin antibody binding). Inhibition by 3 mM adenosine 5'-diphosphate, adenosine 5'-monophosphate, guanosine 5'-triphosphate, pyrophosphate, beta-gamma-methyleneadenosine 5'-triphosphate, adenosine 5'-O-(3 thiotriphosphate), and dibutiryl cyclic adenosine 5'-monophosphate was 74%, 4%, 38%, 46%, 65%, 36%, and 0%, respectively, of that produced by 3 mM ATP. Kinetic analysis of ATP inhibition suggested an allosteric effect as the plot of 1/v (insulin degradation) versus ATP concentration was not linear and the Hill coefficient was more than 1 (1.51 and 2.44). The binding constant for allosteric inhibition was KiT = 1.5 x 10(-7) M showing a decrease of enzyme affinity induced by ATP. We conclude that ATP has an inhibitory effect on the insulin degradation activity of the enzyme.

From mild to severe primary hyperparathyroidism: The Brazilian experience.

Abstract: Primary hyperparathyroidism often presents as an asymptomatic disorder. In our institution, routine serum calcium measurements have now been used as part of medical examination for 23 years. Out of 124 patients consecutively seen at our institution, 47% presented with no symptoms related to the disease, while 25% presented with severe skeletal involvement and osteitis fibrosa cystica, 25% with renal stone disease without overt bone involvement, and 2% with the typical neuropsychiatric syndrome. This same pattern is seen in the city of Sao Paulo. In severe disease pathological fractures are frequently seen, especially in long bones of the lower extremities, and also loss of lamina dura of the teeth and salt-and-pepper appearance of the skull. Bone mineral density is extremely low in these patients but usually show remarkable recovery following surgical cure. Serum PTH and bone markers are considerable higher in severely affected patients, who also have a high rate of vitamin D deficiency, and the parathyroid lesion is easier located compared with asymptomatic patients. From pathological specimens 87% had histological confirmation of a single adenoma, 6.4% multiple gland hyperplasia and 3.8% carcinoma.

Fish School Search

Abstract: Real world problems are packed with complex issues often hard to be computed. Searching for parameters or candidate solutions is frequently associated with these complexities. The reason for that is chiefly related to the large dimensionalities of some search spaces. In general, problems involving large search spaces use traditional computer intensive methods that are, quite often, expensive (i.e. resource consuming). Nature-inspired algorithms, on the other hand, are able to deal reasonably well with the abovementioned difficulties. In this chapter, we provide an overview of a novel approach for searching in high-dimensional spaces based on the behaviors of fish schools. As any other intelligent technique based on population, Fish School Search (FSS) greatly benefits from the collective emerging behavior that increases mutual survivability. Broadly speaking, FSS is composed of operators that can be grouped in the following categories: feeding, swimming and breeding. Together, these operators provide computing behavior such as: (i) high-dimensional search ability, (ii) automatic selection between exploration and exploitation, and (iii) self-adaptable guidance towards sought solutions. This chapter seeks to explain the main ideas behind FSS to researchers and practitioners. In addition, we include examples and simulations aimed at clarifying the simplicity and potentials of FSS.