Major depression and disease activity among systemic lupus erythematosus Egyptian females

AbstractAim of the workThe aim of this study was to identify the relationship between disease activity in SLE Egyptian females and the presence, severity and pattern of major depression in these patients.Patients and methodsThe study sample included 100 female patients; fifty SLE patients and fifty healthy adults with matching age serving as control. Patients were assessed using Beck Inventory Score for the presence of major depression, SLEDAI to determine disease activity, SLICC/ACR damage index and HAQ score for functional disability.ResultsThe majority of patients had symptoms of major depression 32/50 (64%) based on Beck Inventory Score while in controls only 16/50 (36%) had major depression. The most common depressive symptoms in SLE patients were: Guilty feeling (92%), Self-dislike (91.6%), Self-criticalness (90.4%), Crying spells (87.5%), Loss of pleasure (83.3%), Change in appetite (83.3%), Agitation (82.8%) and Pessimism (82%). Patients with major depression presented a trend toward having greater severity of SLE disease activity compared with those without major depression (p=0.04). The presence of major depression was significantly associated with functional disability measured by HAQ score (p=0.01). The patients with major depression did not differ significantly from patients without major depression regarding their steroid dosage (p=0.55), SLICC/ACR damage score (p=0.16) and disease duration (p=0.69) but differed significantly as regards Beck Hopelessness Scale (p<0.0001) and suicidal ideation score (p=0.009).ConclusionMajor depression was highly presented in Egyptian SLE patients (64%); its severity was associated with disease activity, but not with steroid administration, cumulative damage or disease duration

Moving On:Measuring Movement Remotely after Stroke

Most persons with stroke suffer from motor impairment, which restricts mobility on one side, and affects their independence in daily life activities. Measuring recovery is needed to develop individualized therapies. However, commonly used clinical outcomes suffer from low resolution and subjectivity. Therefore, objective biomechanical metrics should be identified to measure movement quality. However, non-portable laboratory setups are required in order to measure these metrics accurately. Alternatively, minimal wearable systems can be developed to simplify measurements performed at clinic or home to monitor recovery. Thus, the goal of the thesis was ‘To identify metrics that reflect movement quality of upper and lower extremities after stroke and develop wearable minimal systems for tracking the proposed metrics’. Section Upper Extremity First, we systematically reviewed literature ( Chapter II ) to identify metrics used to measure reaching recovery longitudinally post-stroke. Although several metrics were found, it was not clear how they differentiated recovery from compensation strategies. Future studies must address this gap in order to optimize stroke therapy. Next, we assessed a ‘valid’ measure for smoothness of upper paretic limb reaching ( Chapter III ), as this was commonly used to measure movement quality. After a systematic review and simulation analyses, we found that reaching smoothness is best measured using spectral arc length. The studies in this section offer us a better understanding of movement recovery in the upper extremity post-stroke. Section Lower Extremity Although metrics that reflect gait recovery are yet to be identified, in this section we focused on developing minimal solutions to measure gait quality. First, we showed the feasibility of 1D pressure insoles as a lightweight alternative for measuring 3D Ground Reaction Forces (GRF) ( Chapter IV ). In the following chapters, we developed a minimal system; the Portable Gait Lab (PGL) using only three Inertial Measurement Units (IMUs) (one per foot and one on the pelvis). We explored the Centroidal Moment Pivot (CMP) point ( Chapter V ) as a biomechanical constraint that can help with the reduction in sensors. Then, we showed the feasibility of the PGL to track 3D GRF ( Chapters VI-VII ) and relative foot and CoM kinematics ( Chapter VIII-IX ) during variable overground walking by healthy participants. Finally, we performed a limited validation study in persons with chronic stroke ( Chapter X ). This thesis offers knowledge and tools which can help clinicians and researchers understand movement quality and thereby develop individualized therapies post-stroke

Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting

Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.</p

Towards Wearable Electromyography for Personalized Musculoskeletal Trunk Models using an Inverse Synergy-based Approach

Electromyography (EMG)-driven musculoskeletal models (EMS) of the trunk are used for estimating lumbosacral joint moments and compressive loads during lifting tasks. These models provide personalized estimates of the parameters using information from many sensors. However, to advance technology from labs to workplaces, there is a need for sensor reduction to improve wearability and applicability. Therefore we introduce an EMG sensor reduction approach based on inverse synergy extrapolation, to reconstruct unmeasured EMG signals for different box-lifting techniques. 12 participants performed an array of tasks (squat, stoop, unilateral twist and bilateral twist) with different weights (0 kg, 7.5 kg and 15 kg). We found that two synergies were sufficient to explain the different lifting tasks (median variance accounted for of 0.91). Building upon this, we used two sensors at optimal subject-specific muscle locations to reconstruct the EMG of four unmeasured channels. Evaluation of the reconstructed and reference EMG showed median coefficients of determination (R2) between 0.70 and 0.86, with median root mean squared errors (RMSE) ranging from 0.02 to 0.04 relative to maximal voluntary contraction. This indicates that our proposed method shows promise for sensor reduction for driving a trunk EMS for ambulatory biomechanical risk assessment in occupational settings and exoskeleton control

Towards Real-Time Decoding of Motor Unit Firing Events and Resulting Muscle Activation During Human Locomotion and High-Force Contractions

Interfacing with the central nervous system is es-sential for developing personalized neuro-rehabilitation strategies. High-density electromyography (HD-EMG), together with blind source separation (BSS) techniques, enables decoding motor unit (MU) firing activity in a non-invasive manner. However, traditional BSS decomposition techniques are limited to isometric contractions and yield suboptimal results during high-force trials. Additionally, the challenge lies not solely in the decomposition of MUs but also in linking MU firing patterns to their twitch characteristics to decode resultant joint moments accurately. In this work, we introduce a novel, real-time capable neuromuscular framework that enables de-coding accurate firing events and their associated activation profiles simultaneously during both walking and high-force trials. First, we propose a two-stage decomposition to estimate MU filters tailored to walking and high-force HD-EMG data (from the soleus and the tibialis anterior muscles, respectively). Second, we estimate optimal twitch responses that provide accurate MU-specific activation dynamics. For the walking trial, results showed that the estimated activation profiles exhibited a stronger resemblance to the reference moment $(R2=0.89)$ than conventional EMG envelopes $(R2=0.86)$. This suggests that both the decomposition and the estimation of MU twitch properties successfully estimated muscle activation. For the high-force trial (90% of maximum voluntary contraction [MVC]), results showed a broader diversity of decoded MUs with recruitment thresholds ranging from 20% to 70%MVC. Moreover, our algorithms operated within times (&lt; 9 ms) well below the neuromechanical delay. Our study presents for the first time an online-ready method-ology to decode MUs from the soleus muscle during walking with sufficient detail to account for observed ankle moment trends. This has multiple implications for developing neuro-rehabilitation devices that can adapt more effectively to a patient's individual needs and progress.</p