The first reported results in Brazilian populations support the role of inflammasome in the development of RA.

Wrist actigraphy (ACT) is a low-cost and well-established technique for long-term monitoring of human activity. It has a special relevance in sleep studies, where its noninvasive nature makes it a valuable tool for behavioral characterization and for the detection and diagnosis of some sleep disorders. The traditional sleep/wakefulness state estimation algorithms from the nocturnal ACT data are unbalanced from a sensitivity and specificity points of view since they tend to overestimate sleep state, with severe consequences from a diagnosis point of view. They usually maximize the overall accuracy that does not take into account the highly unbalanced state distribution. In this paper, a method is proposed to appropriately deal with this unbalanced problem, achieving similar sensitivity and specificity scores in the state estimation process. The proposed method combines two linear discriminant classifiers, trained with two different criteria involving movement detection to generate a first state estimate. This result is then refined by a Hidden Markov Model-based algorithm. The global accuracy, the sensitivity, and the specificity of the method are 77.8%, 75.6%, and 81.6%, respectively, performing better than the tested algorithms. If the performance is assessed only for movement periods, this improvement is even higher.

Uncaria tomentosa (Willd.) DC (Rubiaceae) is a large woody vine that is native to the Amazon and Central American rainforests and is used widely in traditional medicine for its immunomodulatory and antiinflammatory activities. The present work used in vivo immunotoxic and in vitro immunomodulatory experiments to investigate the effects of a pentacyclic oxindole alkaloid extract from U. tomentosa bark on lymphocyte phenotype, Th1/Th2 cytokine production, cellular proliferation and cytotoxicity. For the in vivo immunotoxicity testing, BALB/c male mice were treated once a day with 125, 500 or 1250 mg/kg of U. tomentosa extract for 28 days. For the in vitro protocol, lymphocytes were cultured with 10-500 μg/mg of the extract for 48 h. The extract increased the cellularity of splenic white pulp and the thymic medulla and increased the number of T helper lymphocytes and B lymphocytes. Also, a large stimulatory effect on lymphocyte viability was observed. However, mitogen-induced T lymphocyte proliferation was significantly inhibited at higher concentrations of U. tomentosa extract. Furthermore, an immunological polarization toward a Th2 cytokine profile was observed. These results suggest that the U. tomentosa aqueous-ethanol extract was not immunotoxic to mice and was able to modulate distinct patterns of the immune system in a dose-dependent manner.

The extract was effective to prevent the progression of immune-mediated diabetes by distinct pathways.

The automatic computation of the hypnogram and sleep Parameters, from the data acquired with portable sensors, is a challenging problem with important clinical applications. In this paper, the hypnogram, the sleep efficiency (SE), rapid eye movement (REM), and nonREM (NREM) sleep percentages are automatically estimated from physiological (ECG and respiration) and behavioral (Actigraphy) nocturnal data. Two methods are described; the first deals with the problem of the hypnogram estimation and the second is specifically designed to compute the sleep parameters, outperforming the traditional estimation approach based on the hypnogram. Using an extended set of features the first method achieves an accuracy of 72.8%, 77.4%, and 80.3% in the detection of wakefulness, REM, and NREM states, respectively, and the second an estimation error of 4.3%, 9.8%, and 5.4% for the SE, REM, and NREM percentages, respectively.