No abstract

The objective of the work described in this paper was to evaluate mitochondrial abnormalities in perifascicular atrophic fibers in muscle biopsies from patients with dermatomyositis (DM). We localized cytochrome c oxidase (COX) and succinate dehydrogenase (SDH) histochemically in muscle biopsies of 12 patients with DM, and 12 control patients with neurogenic atrophy. These two histochemical techniques were also combined on single tissue sections in order to accentuate any COX-negative fibers. Eleven out of 12 patients (91.6%) with DM showed histochemical evidence of mitochondrial dysfunction in perifascicular distribution. Similar abnormalities in histochemical staining were not seen in comparably sized myofibers that were atrophic due to denervation. It is concluded that abnormal SDH and COX histochemical activities in atrophic perifascicular fibers are characteristic of dermatomyositis. These abnormal staining characteristics could not be accounted for solely by myofiber atrophy, or by generalized abnormalities in histochemical staining.

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Physiological signal measurement and processing is increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to this motion artifacts, which becomes particularly prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated using the difference in the signal to noise ratio (𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥) and percentage reduction in motion artifacts (𝜂𝜂). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average 𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥 and 𝜂𝜂 values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA using 10 IMFs produced an average 𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥 and 𝜂𝜂 values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals’ foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.