Is there a relationship between psoriasis and hepatitis C? A meta-analysis and bioinformatics investigation.

BACKGROUND: The relationship between psoriasis and hepatitis C was previously controversial, so our purpose is to investigate this connection. METHODS: We conducted a systematic review of the case-control, cross-sectional and cohort studies examining the association between psoriasis and hepatitis C in PubMed, EMBASE and Cochrane library databases and investigated the overlapping genes between psoriasis targets and hepatitis C targets using bioinformatics analysis. Based on overlapping genes and hub nodes, we also constructed the protein-protein interaction (PPI) network and module respectively, followed by the pathway enrichment analysis. RESULTS: We included 11 publications that reported a total of 11 studies (8 cross-sectional and 3 case-control). The case-control and cross-sectional studies included 25,047 psoriasis patients and 4,091,631 controls in total. Psoriasis was associated with a significant increase of prevalent hepatitis C (OR 1.72; 95% confidence interval [CI] (1.17-2.52)). A total of 389 significant genes were common to both hepatitis C and psoriasis, which mainly involved IL6, TNF, IL10, ALB, STAT3 and CXCL8. The module and pathway enrichment analyses showed that the common genes had the potential to influence varieties of biological pathways, including the inflammatory response, cytokine activity, cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, which play an important role in the pathogenesis of hepatitis C and psoriasis. CONCLUSION: Patients with psoriasis display increased prevalence of hepatitis C and the basic related mechanisms between hepatitis C and psoriasis had been preliminarily clarified.

Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons.

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors-specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and -5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side's mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.

Joint Kinematics, Kinetics and Muscle Synergy Patterns During Transitions Between Locomotion Modes.

There is an increasing demand for accurately predicting human movement intentions. To be effective, predictions must be performed as early as possible in the preceding step, though precisely how early has been studied relatively little; how and when a person's movement patterns in a transition step deviate from those in the preceding step must be clearly defined. In this study, we collected motion kinematics, kinetics and electromyography data from 9 able-bodied participants during 7 locomotion modes. Twelve types of steps between the 7 locomotion modes were studied, including 5 continuous steps (taking another step in the same locomotion mode) and 7 transitions steps (taking a step from one locomotion mode into another). For each joint degree of freedom, joint angles, angular velocities, moments, and moment rates were compared between continuous steps and transition steps, and the relative timing during the transition step at which these parameters diverged from those of a continuous step, which we refer to as transition starting times, were identified using multiple analyses of variance. Muscle synergies were also extracted for each step, and we studied in which locomotion modes these synergies were common (task-shared) and in which modes they were specific (task-specific). The transition starting times varied among different transitions and joint degrees of freedom. Most transitions started in the swing phase of the transition step. These findings can be applied to determine the critical timing at which a powered assistive device must adapt its control to enable safe and comfortable support to a user.

A Method of Detecting Human Movement Intentions in Real Environments.

Accurate and timely movement intention detection can facilitate exoskeleton control during transitions between different locomotion modes. Detecting movement intentions in real environments remains a challenge due to unavoidable environmental uncertainties. False movement intention detection may also induce risks of falling and general danger for exoskeleton users. To this end, in this study, we developed a method for detecting human movement intentions in real environments. The proposed method is capable of online self-correcting by implementing a decision fusion layer. Gaze data from an eye tracker and inertial measurement unit (IMU) signals were fused at the feature extraction level and used to predict movement intentions using 2 different methods. Images from the scene camera embedded on the eye tracker were used to identify terrains using a convolutional neural network. The decision fusion was made based on the predicted movement intentions and identified terrains. Four able-bodied participants wearing the eye tracker and 7 IMU sensors took part in the experiments to complete the tasks of level ground walking, ramp ascending, ramp descending, stairs ascending, and stair descending. The recorded experimental data were used to test the feasibility of the proposed method. An overall accuracy of 93.4% was achieved when both feature fusion and decision fusion were used. Fusing gaze data with IMU signals improved the prediction accuracy.

An Integrated Eye-Tracking and Motion Capture System in Synchronized Gaze and Movement Analysis.

Integrating mobile eye-tracking and motion capture emerges as a promising approach in studying visual-motor coordination, due to its capability of expressing gaze data within the same laboratory-centered coordinate system as body movement data. In this paper, we proposed an integrated eye-tracking and motion capture system, which can record and analyze temporally and spatially synchronized gaze and motion data during dynamic movement. The accuracy of gaze measurement were evaluated on five participants while they were instructed to view fixed vision targets at different distances while standing still or walking towards the targets. Similar accuracy could be achieved in both static and dynamic conditions. To demonstrate the usability of the integrated system, several walking tasks were performed in three different pathways. Results revealed that participants tended to focus their gaze on the upcoming path, especially on the downward path, possibly for better navigation and planning. In a more complex pathway, coupled with more gaze time on the pathway, participants were also found having the longest step time and shortest step length, which led to the lowest walking speed. It was believed that the integration of eye-tracking and motion capture is a feasible and promising methodology quantifying visual-motor coordination in locomotion.

A Muscle Synergy-Inspired Method of Detecting Human Movement Intentions Based on Wearable Sensor Fusion.

Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method of locomotion mode identification by fusing the electromyography data with two types of data from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From the finite state machine perspective, the enhanced method was used to systematically identify 2 static modes, 7 dynamic modes, and 27 transitions among them. In addition to the five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), we identified the transition between different walking speeds and modes of ramp walking at different inclination angles. Seven combinations of sensor fusion were conducted, on experimental data from 8 able-bodied adult subjects, and their classification accuracy and prediction time were compared. Prediction based on a fusion of electromyography and gyroscope (angular velocity) data predicted transitions earlier and with higher accuracy. All transitions and modes were identified with a total average classification accuracy of 94.5% with fused sensor data. For nearly all transitions, we were able to predict the next locomotion mode 300-500ms prior to the step into that mode.

[Clinical analysis of 73 cases of hypoglycemia with brain dysfunctions].

OBJECTIVE: To identify the clinical features and pathogenesis of hypoglycemia with brain dysfunctions as the main manifestations. METHOD: A retrospective analysis of 73 cases with brain dysfunctions caused by hypoglycemia was performed. RESULT: Hypoglycemic brain dysfunctions were mainly caused by poor control of the dosage of hypoglycemic agents in diabetic patients, and the major clinical manifestations included coma, hemiparalysis, epilepsy and mental disorders. The pathogenesis was complicated, possibly related to a variety of factors such as rapid declination of blood sugar level, aging, cerebral arteriosclerosis, and hypoglycemic cerebral vasospasm or selective nerve injury. CONCLUSIONS: A blood sugar test should be performed for any patients with brain dysfunctions of unidentified causes for early diagnosis and treatment. In addition, diabetic patients should take hypoglycemic agent adequately. Regular monitoring of blood sugar level is key to the prevention of hypoglycemia.

[Spirometra mansoni infection in an infant: one case report].

This paper reports the diagnosis and therapy of one case of Spirometra mansoni infection in an infant.

[Event-related potentials of relational memory in target and delay phases of working memory].

OBJECTIVE: To compare the spatiotemporal pattern of event-related potentials (ERPs) of relational memory retention between color-location binding and semantics-location binding. METHODS: The ERPs of 19 channels were recorded from 14 healthy subjects while performing the two binding delayed-match-to-sample tasks. A paired-sample t test was used for analysis of both the behavioral performance and ERPs, and statistical parametric mapping (SPM) of the t values was applied to ERPs. RESULTS: Behavioral performances (reaction time and accuracy) in color-location binding were significant better than those of semantics-location binding. The SPM(t) of the ERPs showed significant effects in the middle frontal region and most areas of the parietal lobe (about 200 ms), the bilateral prefrontal and frontal regions (260 approximately 320 ms), and the left occipital region (500 approximately 580 ms). CONCLUSION: Retention of semantics-location relation involves more brain regions than that of color-location relation, and the largest effect occurs in the frontal region.

[Nestin activation after rat cerebral ischemia-reperfusion injury and its changes in response to Tongxinluo treatment].

OBJECTIVE: To investigate nestin activation in rat brain subjected to ischemia-reperfusion injury and its changes in response to Tongxinluo treatment. METHODS: Cerebral ischemia was induced by temporary middle cerebral artery occlusion (MCAO) in rats. At 3, 7, 14 and 21 days after MCAO, nestin expression in the ependyma, subventricular zone (SVZ), hippocampal subdentate gyrus zone (HDG) of the rats treated with Tongxinluo were guantified by immunohistochemistry. RESULTS: Compared with the sham operation group, nestin was significantly increased 7, 14 and 21 days after MCAO (P<0.05), and immunofluorescence of BrdU+nestin-positive neurons significantly increased in the SVZ. After treatment with Tongxinluo, the number of BrdU-positive neurons and BrdU+nestin-positive neurons significantly increased as compared with MCAO group (P<0.05). CONCLUSION: Focal cerebral ischemia in the rat results in rapid response and proliferation of neural stem cells in the SVZ and HDG in the ischemic hemisphere, and Tongxinluo may enhance the differentiation and proliferation capacity of the neural stem cells after MCAO.