The results of this pilot study suggest that the combination of TENS with tDCS has a superior effect compared with tDCS alone.

No abstract

In this work, we demonstrate the first example of fully printed carbon nanomaterials on paper with unique features, aiming the fabrication of functional electronic and electrochemical devices. Bare and modified inks were prepared by combining carbon black and cellulose acetate to achieve high-performance conductive tracks with low sheet resistance. The carbon black tracks withstand extremely high folding cycles (>20 000 cycles), a new record-high with a response loss of less than 10%. The conductive tracks can also be used as 3D paper-based electrochemical cells with high heterogeneous rate constants, a feature that opens a myriad of electrochemical applications. As a relevant demonstrator, the conductive ink modified with Prussian-blue was electrochemically characterized proving to be very promising toward the detection of hydrogen peroxide at very low potentials. Moreover, carbon black circuits can be fully crumpled with negligible change in their electrical response. Fully printed motion and wearable sensors are additional examples where bioinspired microcracks are created on the conductive track. The wearable devices are capable of efficiently monitoring extremely low bending angles including human motions, fingers, and forearm. Here, to the best of our knowledge, the mechanical, electronic, and electrochemical performance of the proposed devices surpasses the most recent advances in paper-based devices.

The visualization of complex 3D images remains a challenge, a fact that is magnified by the difficulty to classify or segment volume data. In this paper, we introduce size-based transfer functions, which map the local scale of features to color and opacity. Features in a data set with similar or identical scalar values can be classified based on their relative size. We achieve this with the use of scale fields, which are 3D fields that represent the relative size of the local feature at each voxel. We present a mechanism for obtaining these scale fields at interactive rates, through a continuous scale-space analysis and a set of detection filters. Through a number of examples, we show that size-based transfer functions can improve classification and enhance volume rendering techniques, such as maximum intensity projection. The ability to classify objects based on local size at interactive rates proves to be a powerful method for complex data exploration.

A incapacidade relacionada à dor lombar crônica (DLC) é um fenômeno complexo e multifatorial. O objetivo desse estudo foi identificar a prevalência e os fatores associados à incapacidade em pacientes com dor lombar crônica. Estudo transversal com amostra composta por 177 pacientes com DLC, de três serviços de saúde; que responderam ao formulário com dados demográficos, ao Inventário de Depressão de Beck, às Escalas Oswestry Disability Index, de autoeficácia para dor crônica, Tampa de Cinesiofobia e de Fadiga de Piper. A prevalência de incapacidade foi de 65% (IC95%: 57,5 - 72,0) e era de moderada a grave em 80,7% dos pacientes. O modelo de regressão múltipla identificou três fatores independentemente associados à incapacidade: ausência de trabalho remunerado, autoeficácia baixa e depressão. Os fatores associados à incapacidade identificados são modificáveis. Intervenções como recolocação no trabalho, tratamento para a depressão e reconceitualização da crença de autoeficácia podem ter um impacto importante na prevenção e redução de incapacidade.