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Influence associated with Ft . Kind around the Scientific Results of Noninvasive Medical procedures pertaining to Metatarsalgia. A Prospective Pilot Research.
4) solution. This biosensor exhibited good analytical performance with a linear range from 2 to 8 mM glucose and sensitivity of 0.017 μA mM-1. Conclusion The experimental results indicate that Ti-MG thin film has a high ability to electron transfer and glucose oxidation for the development of non-enzymatic glucose biosensors. Copyright © 2020 Journal of Medical Signals & Sensors.Background The tracking devices could help measuring the heart rate and energy expenditure and recognizing the user's activity. The calorie measurement is a significant achievement for the fitness tracking and the continuous health monitoring. Methods In this paper, a combination of an accelerometer and a photoplethysmography (PPG) sensor is implemented to calculate the calories consumed. These sensors were mounted next to each other and then were placed on the ankle and finger by flat cable. The sensed data are transferred via Bluetooth to a smartphone in a serial and real-time manner. An Android App is designed to display the user's health data. The average amount of consumed energy is obtained from the combination of the accelerometer sensor based on the laws of motion and the PPG sensor based on the heart rate data. Results The designed system is tested on 10 nonathlete males and 10 nonathlete females randomly. By applying the wavelet, the value of the acceleration signal variance was reduced from 3.2 to 0.8. The correlation between PPG and pulse oximeter was 0.9. Moreover, the correlation of the accelerometer and treadmill was 0.9. The root mean square error (RMSE) and the P value of the calorie output from PPG and pulse oximeter are 0.53 and 0.008, respectively. The RMSE and the P value of the calories output from the accelerometer and the treadmill are 0.42 and 0.007, respectively. Conclusion Our device validity and reliability were good by comparing it with a typical smart band, smart watch, and smartphone available in the market. The combined PPG and the accelerometer sensors were compared with the gold standard, the pulse oximeter, and the treadmill. According to the results, there is no significant difference in the values obtained. Therefore, a mobile system is augmented with the wireless accelerometer and PPG that are connected to a smartphone. The system could be carried out with the user at any time and any place. Copyright © 2020 Journal of Medical Signals & Sensors.Background Breast cancer is one of the most common cancers in women. Mammogram images have an important role in the treatment of various states of this cancer. In recent years, machine learning methods have been widely used for tumor segmentation in mammogram images. Pixel-based segmentation methods have been presented using both supervised and unsupervised learning approaches. Supervised learning methods are usually fast and accurate, but they usually use a large number of labeled data. Besides, providing these samples is very hard and usually expensive. Unsupervised learning methods do not require the labels of the training data for decision making and they completely ignore the prior knowledge that may lead to a low performance. Semi-supervised learning methods which use a small number of labeled data solve the problem of providing the high number of samples in supervised methods, while they usually result in a higher accuracy in comparison to the unsupervised methods. Methods In this study, we used a semisupervised method for tumor segmentation in which the pixel information is used for the classification. The static and gray level run length matrix features for each pixel are considered as the features, and Fisher discriminant analysis (FDA) is used for feature reduction. A cotraining algorithm based on support vector machine and Bayes classifiers is proposed for tumor segmentation on MIAS data set. Results and Conclusion The results show that the proposed method outperforms both supervised methods. Copyright © 2020 Journal of Medical Signals & Sensors.Background Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line-area integral model (AIM) that highlights the advantages of both LIM and AIMs. Methods The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. AUNP-12 supplier For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM). Results The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P less then 0.05), though computations were 4.9 times more intensive than the proposed LIM. Conclusion We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM-AIM that achieves significantly improved images though with its computational requirements. Copyright © 2020 Journal of Medical Signals & Sensors.Road traffic injuries are the leading cause of death in Qatar but their epidemiology in children has not been fully described. This paper will describe the epidemiology of pediatric road traffic injuries (pRTIs) in Qatar, in order to understand the relationships among risk factors, mechanisms of injury, use of safety equipment, and according to child developmental stages. The primary sample for this study was drawn from all pRTIs (0-18 years) from January 2010 to December 2012-motor vehicle occupants, passengers and drivers, pedestrians, cyclists, motorcyclists, and all-terrain vehicle (ATV) drivers and passengers-seen at the trauma registry of the Hamad Trauma Center, the national Level I Trauma Referral Center of Qatar. During those two years, the Trauma Center attended to 4864 patients, 443 (9.1%) of whom were pRTIs, 83% were male, and 71% were non-Qatari. Only 1.2% of injured passengers and drivers were restrained. All fatalities were passengers or drivers; the overall mortality rate was 3.4%. The motor vehicle crash (MVC) mortality rate was 6.
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