The role of intrinsic muscle mechanics in the neuromuscular control of stable running in the guinea fowl

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66308/1/jphysiol.2009.171017.pd

SpeB of Streptococcus pyogenes Differentially Modulates Antibacterial and Receptor Activating Properties of Human Chemokines

BACKGROUND: CXC chemokines are induced by inflammatory stimuli in epithelial cells and some, like MIG/CXCL9, IP-10/CXCL10 and I-TAC/CXCL11, are antibacterial for Streptococcus pyogenes. METHODOLOGY/PRINCIPAL FINDINGS: SpeB from S. pyogenes degrades a wide range of chemokines (i.e. IP10/CXCL10, I-TAC/CXCL11, PF4/CXCL4, GROalpha/CXCL1, GRObeta/CXCL2, GROgamma/CXCL3, ENA78/CXCL5, GCP-2/CXCL6, NAP-2/CXCL7, SDF-1/CXCL12, BCA-1/CXCL13, BRAK/CXCL14, SRPSOX/CXCL16, MIP-3alpha/CCL20, Lymphotactin/XCL1, and Fractalkine/CX3CL1), has no activity on IL-8/CXCL8 and RANTES/CCL5, partly degrades SRPSOX/CXCL16 and MIP-3alpha/CCL20, and releases a 6 kDa CXCL9 fragment. CXCL10 and CXCL11 loose receptor activating and antibacterial activities, while the CXCL9 fragment does not activate the receptor CXCR3 but retains its antibacterial activity. CONCLUSIONS/SIGNIFICANCE: SpeB destroys most of the signaling and antibacterial properties of chemokines expressed by an inflamed epithelium. The exception is CXCL9 that preserves its antibacterial activity after hydrolysis, emphasizing its role as a major antimicrobial on inflamed epithelium

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties.

INTRODUCTION: Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin's surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements. METHOD: A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain. RESULTS: The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°- 90°) could be reduced by increasing the IED (25-30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm. CONCLUSION: Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles)

Gender differences in gait kinematics for patients with knee osteoarthritis

BACKGROUND: Females have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered a factor in the aetiology of knee OA. However, few studies have investigated sex-related differences in walking mechanics for patients with knee OA and of those, conflicting results have been reported. Therefore, this study was designed to examine the differences in gait kinematics (1) between male and female subjects with and without knee OA and (2) between healthy gender-matched subjects as compared with their OA counterparts. METHODS: One hundred subjects with knee OA (45 males and 55 females) and 43 healthy subjects (18 males and 25 females) participated in this study. Three-dimensional kinematic data were collected during treadmill-walking and analysed using (1) a traditional approach based on discrete variables and (2) a machine learning approach based on principal component analysis (PCA) and support vector machine (SVM) using waveform data. RESULTS: OA and healthy females exhibited significantly greater knee abduction and hip adduction angles compared to their male counterparts. No significant differences were found in any discrete gait kinematic variable between OA and healthy subjects in either the male or female group. Using PCA and SVM approaches, classification accuracies of 98–100 % were found between gender groups as well as between OA groups. CONCLUSIONS: These results suggest that care should be taken to account for gender when investigating the biomechanical aetiology of knee OA and that gender-specific analysis and rehabilitation protocols should be developed