Introduction Care Considerations supported by the Centers for Disease Control and Prevention for the management of Duchenne muscular dystrophy were published in 2010, but there has been limited study of implementation in the United States.Methods A questionnaire collecting information about standard care practices and perceived barriers was piloted by 9 clinic directors of facilities within the Muscular Dystrophy Surveillance, Tracking and Research network.Results Six clinic directors completed the questionnaire; 1 adult-only clinic was excluded.Over 80% adherence was found for 30 of 55 recommendations examined.Greatest variability was for initiation of corticosteroids, bone health monitoring, type of pulmonary function testing, and psychosocial management.Barriers included unclear guidelines, inadequate time and funding, family-specific barriers and lack of empirical support for some recommendations.Discussion This pilot study showed implementation of the 2010 Care Considerations, except for recommendations based largely on expert consensus.Complete adherence requires more studies and active promotion.
Our objective was to evaluate longitudinal changes in Microsoft Kinect measured upper extremity reachable workspace relative surface area (RSA) versus the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R), ALSFRS-R upper extremity sub-scale and Forced Vital Capacity (FVC) in a cohort of patients diagnosed with amyotrophic lateral sclerosis (ALS). Ten patients diagnosed with ALS (ages 52-76 years, ALSFRS-R: 8-41 at entry) were tested using single 3D depth sensor, Microsoft Kinect, to measure reachable workspace RSA across five visits spanning one year. Changes in RSA, ALSFRS-R, ALSFRS-R upper extremity sub-scale, and FVC were assessed using a linear mixed model. Results showed that upper lateral quadrant RSA declined significantly in one year by approximately 19% (p <0.01) while all other quadrants and total RSA did not change significantly in this time-period. Simultaneously, ALSFRS-R upper extremity sub-scale worsened significantly by 25% (p <0.01). In conclusion, upper extremity reachable workspace RSA as a novel ALS outcome measure is capable of objectively quantifying declines in upper extremity ability over time in patients with ALS with more granularity than other common outcome measures. RSA may serve as a clinical endpoint for the evaluation of upper extremity targeted therapeutics.
Artificial nanoreactors that can facilitate catalysis in living systems on-demand with the aid of a remotely operable and biocompatible energy source are needed to leverage the chemical diversity and expediency of advanced chemical synthesis in biology and medicine. Here, we designed and synthesized plasmonically integrated nanoreactors (PINERs) with highly tunable structure and NIR-light-induced synergistic function for efficiently promoting unnatural catalytic reactions inside living cells. We devised a synthetic approach toward PINERs by investigating the crucial role of metal-tannin coordination polymer nanofilm—the pH-induced decomplexation-mediated phase-transition process—for growing arrays of Au-nanospheroid-units, constructing a plasmonic corona around the proximal and reactant-accessible silica-compartmentalized catalytic nanospace. Owing to the extensive plasmonic coupling effect, PINERs show strong and tunable optical absorption in the visible to NIR range, ultrabright plasmonic light scattering, controllable thermoplasmonic effect, and remarkable catalysis; and, upon internalization by living cells, PINERs are highly biocompatible and demonstrate dark-field microscpy-based bioimaging features. Empowered with the synergy between plasmonic and catalytic effects and reactant/product transport, facilitated by the NIR-irradiation, PINERs can perform intracellular catalytic reactions with dramatically accelerated rates and efficiently synthesize chemically activated fluorescence-probes inside living cells.
We propose a novel low-cost method for quantitative assessment of upper extremity workspace envelope using Microsoft Kinect camera. In clinical environment there are currently no practical and cost-effective methods available to provide arm-function evaluation in three-dimensional space. In this paper we examine the accuracy of the proposed technique for workspace estimation using Kinect in comparison with a motion capture system. The experimental results show that the developed system is capable of capturing the workspace with sufficient accuracy and robustness.
Human behavioral interventions aimed at improving health can benefit from objective wearable sensor data and mathematical models. Smartphone-based sensing is particularly practical for monitoring behavioral patterns because smartphones are fairly common, are carried by individuals throughout their daily lives, offer a variety of sensing modalities, and can facilitate various forms of user feedback for intervention studies. We describe our findings from a smartphone-based study, in which an Android-based application we developed called CalFit was used to collect information related to young adults' dietary behaviors. In addition to monitoring dietary patterns, we were interested in understanding contextual factors related to when and where an individual eats, as well as how their dietary intake relates to physical activity (which creates energy demand) and psychosocial stress. 12 participants were asked to use CalFit to record videos of their meals over two 1-week periods, which were translated into nutrient intake by trained dietitians. During this same period, triaxial accelerometry was used to assess each subject's energy expenditure, and GPS was used to record time-location patterns. Ecological momentary assessment was also used to prompt subjects to respond to questions on their phone about their psychological state. The GPS data were processed through a web service we developed called Foodscoremap that is based on the Google Places API to characterize food environments that subjects were exposed to, which may explain and influence dietary patterns. Furthermore, we describe a modeling framework that incorporates all of these information to dynamically infer behavioral patterns that may be used for future intervention studies.
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