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Point-of-care devices can analyze or characterize a sample in a short period of time. New technologies in medical science seek integrations of different measurement techniques for a complete analysis. selleck chemicals This study describes the fabrication method, tests, and results of microtechnology as an approach for an integrated rheometer. The portable device measured the average flow velocity to calculate its viscosity. The whole system encompasses a microdevice integrated to a data acquisition system powered by USB and controlled by a full custom software. As a result, we obtained an easy-to-handle and fabricate hand-held microrheometer. The device was tested using Newtonian fluids such as Mili-Q water, aqueous solution of Ethylene-glycol at 40% and 25% and Non-Newtonian blood samples. The whole device can provide the non-linear viscosity of a 0.08ml blood sample in less than 30 seconds, in a wide range of shear rate with an accuracy of 93%. More importantly, due to its detection method and simplicity, it can be enclosed within almost any fluidic microsystem, including biomedical applications.

We explore the feasibility of principal component analysis (PCA) as a form of spectral imaging in photon-counting CT.

Using the data acquired by a prototype system and simulated by computer, we investigate the feasibility of spectral imaging in photon-counting CT via PCA for feature extraction and study the impacts made by data standardization and de-noising on its performance.

The PCA in the projection domain maintains the data consistence that is essential for tomographic image reconstruction and performs virtually the same as that in the image domain. The first three primary components account for more than 99.99% covariance of the data. Within anticipation, the contrast-to-noise ratio (CNR) between the target and background in the first principal component image can be larger than that in the image generated from the data acquired in each energy bin. More importantly, the CNR in the first principal component image may be larger than that in the image formed by the summed data acquired in all energy bins (i.e., the conventional polychromatic CT image). In addition, de-noising can not only reduce the noise in images but also improve the effectiveness/efficiency of PCA in feature extraction.

The PCA in either projection or image domain provides another form of spectral imaging in photon-counting CT that fits the essential requirements on spectral imaging in true color.

The verification of PCA's feasibility in CT as a form spectral imaging and observation of its potential superiority in CNR over conventional polychromatic CT are meaningful in theory and practice.

The verification of PCA's feasibility in CT as a form spectral imaging and observation of its potential superiority in CNR over conventional polychromatic CT are meaningful in theory and practice.Current steerable catheters or guidewires often cannot advance into small diameter vessels due to their large diameters or lack of sharp steering capacity. This paper proposes a hydraulically steerable guidewire with 400 μm diameter, which can access 1 mm diameter vessels whose branching angle is larger than 90 degrees. The designed steering mechanism consists of a flexible eccentric tube with inner micro patterns, which can bend in two different curvatures when pressurized. Its distal sharp curve of the 2 mm segment allows access to small diameter vessels because it provides a large steering angle even in confinement inside the narrow vessels. Its proximal gradual curve of the 9 mm segment allows access to relatively large diameter vessels because of its large steering distance. Fabrication of the steering mechanism uses a template and does not use adhesion or division. A 3D printed cylindrical template is patterned by stamping and chemically removed after silicone coating. The performance of selective insertion of the proposed guidewire is evaluated in a blood circulatory system specially developed to mimic human arterial environment. It emulates viscosity, pressure, and flow velocity inside the blood vessels as well as bifurcation geometry. Experiment result shows that the proposed guidewire can access 1 mm diameter vessels with 128 degrees of bifurcation angle. The developed guidewire uses only biocompatible materials including driving fluid.

Several features of the surface electromyography (sEMG) signal are related to muscle activity and fatigue. However, the time-evolution of these features are non-stationary and vary between subjects. The aim of this study is to investigate the use of adaptive algorithms to forecast sEMG feature of the trunk muscles.

Shallow models and a deep convolutional neural network (CNN) were used to simultaneously learn and forecast 5 common sEMG features in real-time to provide tailored predictions. This was investigated for up to a 25 second horizon; for 14 different muscles in the trunk; across 13 healthy subjects; while they were performing various exercises.

The CNN was able to forecast 25 seconds ahead of time, with 6.88% mean absolute percentage error and 3.72% standard deviation of absolute percentage error, across all the features. Moreover, the CNN outperforms the best shallow model in terms of a figure of merit combining accuracy and precision by at least 30% for all the 5 features.

Even though the sEMG features are non-stationary and vary between subjects, adaptive learning and forecasting, especially using CNNs, can provide accurate and precise forecasts across a range of physical activities.

The proposed models provide the groundwork for a wearable device which can forecast muscle fatigue in the trunk, so as to potentially prevent low back pain. Additionally, the explicit real-time forecasting of sEMG features provides a general model which can be applied to many applications of muscle activity monitoring, which helps practitioners and physiotherapists improve therapy.

The proposed models provide the groundwork for a wearable device which can forecast muscle fatigue in the trunk, so as to potentially prevent low back pain. Additionally, the explicit real-time forecasting of sEMG features provides a general model which can be applied to many applications of muscle activity monitoring, which helps practitioners and physiotherapists improve therapy.