Notes

Clinico-pathological profiling involving peripheral ossifying fibroma of the mouth.
Conclusion Our data demonstrate the protective effect of SLIGRL in airways; however, this effect appears to be mediated independently of prostanoids, nitric oxide, circulating adrenaline, ATP-sensitive K + channels, and vagal stimulation. Copyright © 2019 Journal of Microscopy and Ultrastructure.Mesenchymal tumors of the thyroid are extremely rare. Only few isolated cases of primary thyroid granular cell tumor (GrCT) have been reported. The anatomic location of this lesion plays an important role in the differential diagnosis. It is well-known that GrCT commonly involves the head-and-neck region, lower extremity, nuchal region, chest wall, and internal viscera such as the gastrointestinal tract. However, primary GrCT of the thyroid are unexpected and might lead to misdiagnosis, especially with pathological diagnosis limitations such as frozen section and fine-needle aspiration. We believe that it is important to establish a good differential diagnosis because of its ability to simulate the appearance of invasive carcinoma, especially in cases lacking tissue block examination. In this paper, we try to focus on clinical, radiological potential characteristics, and the differential diagnosis of the tumor. Copyright © 2019 Journal of Microscopy and Ultrastructure.This study presents the design and feasibility testing of an interactive portable motion-analysis device for the assessment of upper-limb motor functions in clinical and home settings. The device engages subjects to perform tasks that imitate activities of daily living, e.g. drinking from a cup and moving other complex objects. Sitting at a magnetic table subjects hold a 3D printed cup with an adjustable magnet and move this cup on the table to targets that can be drawn on the table surface. A ball rolling inside the cup can enhance the task challenge by introducing additional dynamics. A single video camera with a portable computer tracks real-time kinematics of the cup and the rolling ball using a custom-developed, color-based computer-vision algorithm. check details Preliminary verification with marker-based 3D-motion capture demonstrated that the device produces accurate kinematic measurements. Based on the real-time 2D cup coordinates, audio-visual feedback about performance can be delivered to increase motivation. The feasibility of using this device in clinical diagnostics is demonstrated on 2 neurotypical children and also 3 children with upper-extremity impairments in the hospital, where conventional motion-analysis systems are difficult to use. The device meets key needs for clinical practice 1) a portable solution for quantitative motor assessment for upper-limb movement disorders at non-laboratory clinical settings, 2) a low-cost rehabilitation device that can increase the volume of in-home physical therapy, and 3) the device affords testing and training a variety of motor tasks inspired by daily challenges to enhance self-confidence to participate in day-to-day activities. 2168-2372 © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http//www.ieee.org/publications_standards/publications/rights/index.html for more information.We aimed at reducing alarm fatigue in neonatal intensive care units by developing a model using machine learning for the early prediction of critical cardiorespiratory alarms. During this study in over 34,000 patient monitoring hours in 55 infants 278,000 advisory (yellow) and 70,000 critical (red) alarms occurred. Vital signs including the heart rate, breathing rate, and oxygen saturation were obtained at a sampling frequency of 1 Hz while heart rate variability was calculated by processing the ECG - both were used for feature development and for predicting alarms. Yellow alarms that were followed by at least one red alarm within a short post-alarm window constituted the case-cohort while the remaining yellow alarms constituted the control cohort. For analysis, the case and control cohorts, stratified by proportion, were split into training (80%) and test sets (20%). Classifiers based on decision trees were used to predict, at the moment the yellow alarm occurred, whether a red alarm(s) would shortly follow. The best performing classifier used data from the 2-min window before the occurrence of the yellow alarm and could predict 26% of the red alarms in advance (18.4s, median), at the expense of 7% additional red alarms. These results indicate that based on predictive monitoring of critical alarms, nurses can be provided a longer window of opportunity for preemptive clinical action. Further, such as algorithm can be safely implemented as alarms that are not algorithmically predicted can still be generated upon the usual breach of the threshold, as in current clinical practice. 2168-2372 © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http//www.ieee.org/publications_standards/publications/rights/index.html for more information.OBJECTIVE Non-invasive respiration detection methods are of great value to healthcare applications and disease diagnosis with their advantages of minimizing the patient's physical burden and lessen the requirement of active cooperation of the subject. This method avoids extra preparations, reduces environmental constraints, and strengthens the possibility of real-time respiratory detection. Furthermore, identifying abnormal breathing patterns in real-time is necessary for the diagnosis and monitoring of possible respiratory disorders. METHOD A non-invasive method for detecting multiple breathing patterns using C-band sensing technique is presented, which is used for identifying different breathing patterns in addition to extract respiratory rate. We first evaluate the feasibility of this non-contact method in measuring different breathing patterns. Then, we detect several abnormal breathing patterns associated with certain respiratory disorders at real time using C-band sensing technique in indoor environment.
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