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Purpose: We examined changes in urological care delivery due to COVID-19 in the U.S. based on patient, practice, and local/regional demographic and pandemic response features., Materials and Methods: We analyzed real-world data from the American Urological Association Quality (AQUA) Registry collected from electronic health record systems. Data represented 157 outpatient urological practices and 3,165 providers across 48 U.S. states and territories, including 3,297,721 unique patients, 12,488,831 total outpatient visits and 2,194,456 procedures. The primary outcome measure was the number of outpatient visits and procedures performed (inpatient or outpatient) per practice per week, measured from January 2019 to February 2021., Results: We found large (>50%) declines in outpatient visits from March 2020 to April 2020 across patient demographic groups and states, regardless of timing of state stay-at-home orders. Nonurgent outpatient visits decreased more across various nonurgent procedures (49%-59%) than for procedures performed for potentially urgent diagnoses (38%-52%); surgical procedures for nonurgent conditions also decreased more (43%-79%) than those for potentially urgent conditions (43%-53%). African American patients had similar decreases in outpatient visits compared with Asians and Caucasians, but also slower recoveries back to baseline. Medicare-insured patients had the steepest declines (55%), while those on Medicaid and government insurance had the lowest percentage of recovery to baseline (73% and 69%, respectively)., Conclusions: This study provides real-world evidence on the decline in urological care across demographic groups and practice settings, and demonstrates a differential impact on the utilization of urological health services by demographics and procedure type.

Point-of-care biosensor systems can potentially improve patient care through real-time and remote health monitoring. Over the past few decades, research has been conducted in the field of biosensors to detect patterns of biomarkers and provide information on their concentration in biological samples for robust diagnosis. In future point-of-care applications, requirements such as rapid label-free detection, miniaturized sensor size, and portability will limit the types of biosensors that can be used. This paper reviews label-free detection techniques using Biological MicroElectroMechanical Systems as a potential candidate for point-of-care biosensing applications. Furthermore, detailed surveys have been carried out on wireless networking schemes applicable for a point-of-care environment and on prognostic techniques that will enable decision-support services. This paper concludes by providing a list of challenges that must be resolved before realizing biosensor systems for next-generation point-of-care applications.

Surface-recorded somatosensory-evoked potentials (SEPs) are neural signals elicited by an external stimulus. In the case of electrically induced SEPs, the artifact generated by the stimulation process can severely distort the signal. In some cases, the artifact tail often lasts well into the initiation of the SEP making the determination of absolute latency very difficult. In this work, a new approach was taken to identify factors that affect the tail of the artifact. The methodology adopted was the development of a lumped electrical circuit model of the artifact generation process. While the modeling of the instrumentation hardware is relatively simple, this is not the case with tissue and electrode/skin interface effects. Consequently, this paper describes a novel tissue modeling approach that uses an autoregressive moving average (ARMA) parametric technique and an artificial neural network (ANN) to estimate tissue parameters from experimental data. This coupled with an estimation of the stimulation electrode-skin impedance completes the lumped circuit model. Simulink (The Mathworks Inc.) was used to evaluate the model under several different conditions. These results show that both the stimulation electrode-skin interface impedance and nature of the body tissue directly under the recording electrodes have a profound effect on the appearance of the stimulus artifact tail. This was verified by experimentally recorded data obtained from the median nerve using surface electrodes. Conclusions drawn from this work include that stimulation electrodes with low series capacitance should be used whenever possible to minimize the duration of the artifact tail.

A methodology is proposed to segment and label the fundamental activities, namely the first and second heart sounds, S1 and S2of the phonocardiogram (PCG). Information supplementary to the PCG, such as a cue from a synchronously acquired electrocardiogram (ECG), subject-specific prior information, or training examples regarding the activities, is not required by the proposed methodology. A bank of Morlet wavelet correlators is used to obtain a time-scale representation of the PCG. An energy profile of the time-scale representation and a singular value decomposition (SVD) technique are used to identify segments of the PCG that contain the fundamental activities. The robustness of the methodology is demonstrated by the correct segmentation of over 90% of 1068 fundamental activities in a challenging set of PCGs which were recorded from patients with normally functioning and abnormally functioning bioprosthetic valves. The PCGs included highly varying fundamental activities that overlapped in time and frequency with other aberrant non-fundamental activities such as murmurs and noise-like artifacts.