Mechanomyogram for Muscle Function Assessment: A Review

Background

Mechanomyography (MMG) has been extensively applied in clinical and experimental practice to examine muscle characteristics including muscle function (MF), prosthesis and/or switch control, signal processing, physiological exercise, and medical rehabilitation. Despite several existing MMG studies of MF, there has not yet been a review of these. This study aimed to determine the current status on the use of MMG in measuring the conditions of MFs.

Methodology/Principal Findings

Five electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. Two authors independently assessed selected articles using an MS-Word based form created for this review. Several domains (name of muscle, study type, sensor type, subject''s types, muscle contraction, measured parameters, frequency range, hardware and software, signal processing and statistical analysis, results, applications, authors'' conclusions and recommendations for future work) were extracted for further analysis. From a total of 2184 citations 119 were selected for full-text evaluation and 36 studies of MFs were identified. The systematic results find sufficient evidence that MMG may be used for assessing muscle fatigue, strength, and balance. This review also provides reason to believe that MMG may be used to examine muscle actions during movements and for monitoring muscle activities under various types of exercise paradigms.

Conclusions/Significance

Overall judging from the increasing number of articles in recent years, this review reports sufficient evidence that MMG is increasingly being used in different aspects of MF. Thus, MMG may be applied as a useful tool to examine diverse conditions of muscle activity. However, the existing studies which examined MMG for MFs were confined to a small sample size of healthy population. Therefore, future work is needed to investigate MMG, in examining MFs between a sufficient number of healthy subjects and neuromuscular patients.     

Augmentation of Autoantibodies by Helicobacter pylori in Parkinson’s Disease Patients May Be Linked to Greater Severity

Parkinson''s disease (PD) is the second most common chronic and progressive neurodegenerative disorder. Its etiology remains elusive and at present only symptomatic treatments exists. Helicobacter pylori chronically colonizes the gastric mucosa of more than half of the global human population. Interestingly, H. pylori positivity has been found to be associated with greater of PD motor severity. In order to investigate the underlying cause of this association, the Sengenics Immunome protein array, which enables simultaneous screening for autoantibodies against 1636 human proteins, was used to screen the serum of 30 H. pylori-seropositive PD patients (case) and 30 age- and gender-matched H. pylori-seronegative PD patients (control) in this study. In total, 13 significant autoantibodies were identified and ranked, with 8 up-regulated and 5 down-regulated in the case group. Among autoantibodies found to be elevated in H. pylori-seropositive PD were included antibodies that recognize Nuclear factor I subtype A (NFIA), Platelet-derived growth factor B (PDGFB) and Eukaryotic translation initiation factor 4A3 (eIFA3). The presence of elevated autoantibodies against proteins essential for normal neurological functions suggest that immunomodulatory properties of H. pylori may explain the association between H. pylori positivity and greater PD motor severity.     

Unmasking Heavily O-Glycosylated Serum Proteins Using Perchloric Acid: Identification of Serum Proteoglycan 4 and Protease C1 Inhibitor as Molecular Indicators for Screening of Breast Cancer

Heavily glycosylated mucin glycopeptides such as CA 27.29 and CA 15–3 are currently being used as biomarkers for detection and monitoring of breast cancer. However, they are not well detected at the early stages of the cancer. In the present study, perchloric acid (PCA) was used to enhance detection of mucin-type O-glycosylated proteins in the serum in an attempt to identify new biomarkers for early stage breast cancer. Sensitivity and specificity of an earlier developed sandwich enzyme-linked lectin assay were significantly improved with the use of serum PCA isolates. When a pilot case-control study was performed using the serum PCA isolates of normal participants (n = 105) and patients with stage 0 (n = 31) and stage I (n = 48) breast cancer, higher levels of total O-glycosylated proteins in sera of both groups of early stage breast cancer patients compared to the normal control women were demonstrated. Further analysis by gel-based proteomics detected significant inverse altered abundance of proteoglycan 4 and plasma protease C1 inhibitor in both the early stages of breast cancer patients compared to the controls. Our data suggests that the ratio of serum proteoglycan 4 to protease C1 inhibitor may be used for screening of early breast cancer although this requires further validation in clinically representative populations.     

Spontaneous K-Complex Density in Slow-Wave Sleep

Purpose

To study spontaneous K-complex (KC) densities during slow-wave sleep. The secondary objective was to estimate intra-non-rapid eye movement (NREM) sleep differences in KC density.

Materials and Methods

It is a retrospective study using EEG data included in polysomnographic records from the archive at the sleep research laboratory of the Centre for Physiotherapy and Rehabilitation Sciences, Jamia Millia Islamia, India. The EEG records of 4459 minutes were used. The study presents a manual identification investigation of KCs in 17 healthy young adult male volunteers (age = 23.82±3.40 years and BMI = 23.42±4.18 kg/m²).

Results

N3 had a higher KC density than N2 (Z = -2.485, p = 0.013) for all of the probes taken together. Four EEG probes had a higher probe-specific KC density during N3. The inter-probe KC density differed significantly during N2 (χ² = 67.91, p < .001), N3 (χ² = 70.62, p < .001) and NREM (χ² = 68.50, p < .001). The percent distribution of KC decreased uniformly with sleep cycles.

Conclusion

The inter-probe differences during N3 establish the fronto-central dominance of the KC density regardless of sleep stage. This finding supports one local theory of KC generation. The significantly higher KC density during N3 may imply that the neuro-anatomical origin of slow-wave activity and KC is the same. This temporal alignment with slow-wave activity supports the sleep-promoting function of the KC.     

A Lightweight Radio Propagation Model for Vehicular Communication in Road Tunnels

Radio propagation models (RPMs) are generally employed in Vehicular Ad Hoc Networks (VANETs) to predict path loss in multiple operating environments (e.g. modern road infrastructure such as flyovers, underpasses and road tunnels). For example, different RPMs have been developed to predict propagation behaviour in road tunnels. However, most existing RPMs for road tunnels are computationally complex and are based on field measurements in frequency band not suitable for VANET deployment. Furthermore, in tunnel applications, consequences of moving radio obstacles, such as large buses and delivery trucks, are generally not considered in existing RPMs. This paper proposes a computationally inexpensive RPM with minimal set of parameters to predict path loss in an acceptable range for road tunnels. The proposed RPM utilizes geometric properties of the tunnel, such as height and width along with the distance between sender and receiver, to predict the path loss. The proposed RPM also considers the additional attenuation caused by the moving radio obstacles in road tunnels, while requiring a negligible overhead in terms of computational complexity. To demonstrate the utility of our proposed RPM, we conduct a comparative summary and evaluate its performance. Specifically, an extensive data gathering campaign is carried out in order to evaluate the proposed RPM. The field measurements use the 5 GHz frequency band, which is suitable for vehicular communication. The results demonstrate that a close match exists between the predicted values and measured values of path loss. In particular, an average accuracy of 94% is found with R² = 0.86.