Journal of Mechanics in Medicine and Biology

Sit to stand (STS) task requires variability of all body segments to achieve the stability of the important control variables (i.e., center of mass (CM) and head positions). In this study, the possible differences in the variability patterns of various body segments were investigated between 11 chronic low back pain (LBP) and 12 control subjects during STS task through two types of variability analyses; first by calculating the variability of seven limb angles, CM and head positions across 15 trials and second by principal component analysis (PCA) of seven limb angles. Participants performed the task at 3 postural difficulty levels: rigid surface, open eyes (RO), rigid surface, close eyes (RC) and narrow surface, close eyes (NC). The results revealed that LBPs could stabilize the CM and head positions same as controls. Also there was more than 1 synergic combination of whole body segments in both LBP and healthy groups. But the number of PCs accounting for the major part of variance was reduced in the LBPs in the most unstable phase of movement (50%–80% trajectory) in the RO and RC conditions. This may indicate that LBPs have reduced flexibility in the most unstable phase of task.

Passive knee laxity is an important clinical measure to assess function after joint replacement. Clinical observations suggest that the use of minimally invasive surgical techniques in knee arthroplasty may affect the surgeon's ability to orient and position the prosthetic components accurately. Further, recent studies suggest that malplaced prosthetic components in ligament retaining unconstrained unicompartmental knee arthroplasty (UKA) can affect the ligament forces and, hence, the knee laxity. In the present study, a sagittal plane mathematical model of the knee with intact ligaments and unconstrained prosthetic components is used to analyze antero-posterior (A–P) knee laxity during passive flexion at different force levels. Also, the effects of errors in component placement are evaluated. The model calculations show a reasonable agreement with the experimental observations reported in literature. The results show that the A–P laxity during 0°–120° flexion first increases from 0° to about 30°, remains nearly constant for another 10° and then decreases somewhat linearly for higher flexion angles. Some errors in the placement of femoral component of the order of 1 mm can affect the knee laxity by nearly 3 mm in some flexion positions. The analysis has clinical relevance and suggests that the UKA requires close attention to component placement.

In this paper, we experimentally investigated the navigation system of the nonpathogenic strain of E. coli (AW405), and we developed a simulator for the locomotion performance of these swimming nanorobots. The swimming behavior of these robotic cells is sensitive to the chemical gradients in their medium. Tissue and disease cells might produce chemical signals in their surroundings. These chemicals have the potential to affect the locomotion behavior of the bacterial cells. Therefore, bacterial cells can be considered as self-navigator nanorobots that are able to discriminate between disease cells such as cancer. We exploit Bayesian decision theory as a framework in predicting the locomotion behavior of the E. coli robotic cells. Obvious agreement has been achieved between the experimental performance of our moving robotic cells and its corresponding simulation. Our current experimental and theoretical work is considered as a platform to this novel idea of early detection of problematic diseases.

In this paper, we investigated effects of deep brain stimulation (DBS) on Parkinson's disease (PD) when different target sites in the basal ganglia are stimulated. The targets which are investigated are subthalamic nucleus (STN), globus pallidus interna (GPi), and globus pallidus externa (GPe). For this purpose we used a computational model of the basal ganglia-thalamocortical system (BGTCS) with parameters calculated for mean field. This model is able to reproduce both the normal and Parkinsonian activities of basal ganglia, thalamus and cortex in a unified structure. In the present study, we used a mean-field model of the BGTCS, allowing a more complete framework to simulate DBS and to interpret its effects in the BGTCS. Our results suggest that DBS in the STN and GPe could restore the thalamus relay activity, while DBS in the GPi could inhibit it. Our results are compatible with the experimental and the clinical outcomes about the effects of DBS of different targets.

The Journal of Mechanics in Medicine and Biology(JMMB) has retracted the above paper from its April 2012 issue. The Publisher of JMMB was alerted to the fact that the conference paper was previously published in IEEE in 2011. The authors indicated that this manuscript was suggested by the conference organizers for journal publication. As stated clearly in the journal’s guidelines, only original manuscripts will be considered. Once a manuscript is accepted, the author is assumed to cede full copyright of their manuscript over to the publisher - World Scientific Publishing Co.

In this study, we analyzed brain connectivity profiles from 10 mild traumatic brain injury (mTBI) patients and 10 age- and gender-matched normal controls. We computed Granger causality measures from magnetoencephalographic (MEG) activity obtained at the resting state, in an attempt to understand how the default network is affected by mTBI. A connectivity matrix was computed for each subject individually and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fisher's criterion to rank the features and identify the best subset for maximum separation of the groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide a minimum of 85% classification accuracy in separating the two groups, with a sensitivity and specificity of 90% and 80%, respectively. These findings suggest that analysis of functional connectivity patterns may provide a valuable tool for early detection of mTBI.

There is extensive prior research in coronary artery disease (CAD) detection using sounds recorded from the chest. The acoustic approach to CAD detection is cost-effective, noninvasive and easy to implement; however, it is sensitive to external and internal noise commonly encountered in the clinical setting, which can reduce its reliability. If this noise can be accurately detected and eliminated, the accuracy of the acoustic approach to CAD detection can be significantly improved. In this paper, we develop and evaluate an algorithm to detect noise in acoustic recordings made in the clinical setting. This approach uses signals from a reference microphone placed near the stomach that detects both external and internal noise in the acoustic recording. Applying spectral subtraction and a histogram-based threshold to the reference signal, we are able to identify and eliminate segments that contain noise in the acoustic recording. This algorithm is effective in detecting noise from both external and internal sources.

The goal of this paper is to describe some applications of fractional order calculus to biomedical signal processing with emphasis on the ability of this mathematical tool to remove noise, enhance useful information, and generate fractal signals. Three types of digital filters are considered, namely, lowpass differentiation filter, smoothing filter, and 1/f^β-noise generation filter. The filter impulse responses are functions of the fractional order and the sampling period only, and thus can be computed easily. Application examples are presented for illustrations.

Influenza is one of the most important emerging and reemerging infectious diseases, causing high morbidity and mortality in communities (epidemic) and worldwide (pandemic). Here, classification of human vs. non-human influenza, and subtyping of human influenza viral strains virus is done based on profile hidden Markov models (HMM). The classical ways of determining influenza viral subtypes depend mainly on antigenic assays, which is time-consuming and not fully accurate. The introduced technique is much cheaper and faster, yet usually can still yield high accuracy. Multiple sequence alignments were done for the 16 HA subtypes and 9 NA subtypes, followed by profile-HMMs models generation, calibration and evaluation using the HMMER suite for each group. Subtyping accuracy of all HA and NA models achieved 100%, while host classification achieved accuracies around 53% and 95.1% according to HA subtype.