Astrocytic RIPK3 exerts protective anti-inflammatory activity during viral encephalitis via induction of serpin protease inhibitors.

Flaviviruses pose a significant threat to public health due to their ability to infect the central nervous system (CNS) and cause severe neurologic disease. Astrocytes play a crucial role in the pathogenesis of flavivirus encephalitis through their maintenance of blood-brain barrier (BBB) integrity and their modulation of immune cell recruitment and activation within the CNS. We have previously shown that receptor interacting protein kinase-3 (RIPK3) is a central coordinator of neuroinflammation during CNS viral infection, a function that occurs independently of its canonical function in inducing necroptotic cell death. To date, however, roles for necroptosis-independent RIPK3 signaling in astrocytes are poorly understood. Here, we use mouse genetic tools to induce astrocyte-specific deletion, overexpression, and chemogenetic activation of RIPK3 to demonstrate an unexpected anti-inflammatory function for astrocytic RIPK3. RIPK3 activation in astrocytes was required for host survival in multiple models of flavivirus encephalitis, where it restricted neuropathogenesis by limiting immune cell recruitment to the CNS. Transcriptomic analysis revealed that, despite inducing a traditional pro-inflammatory transcriptional program, astrocytic RIPK3 paradoxically promoted neuroprotection through the upregulation of serpins, endogenous protease inhibitors with broad immunomodulatory activity. Notably, intracerebroventricular administration of SerpinA3N in infected mice preserved BBB integrity, reduced leukocyte infiltration, and improved survival outcomes in mice lacking astrocytic RIPK3. These findings highlight a previously unappreciated role for astrocytic RIPK3 in suppressing pathologic neuroinflammation and suggests new therapeutic targets for the treatment of flavivirus encephalitis.

Use of Wavelet Transform to Detect Compensated and Decompensated Stages in the Congestive Heart Failure Patient.

This research work is aimed at improving health care, reducing cost, and the occurrence of emergency hospitalization in patients with Congestive Heart Failure (CHF) by analyzing heart and lung sounds to distinguish between the compensated and decompensated states. Compensated state defines stable state of the patient but with lack of retention of fluids in lungs, whereas decompensated state leads to unstable state of the patient with lots of fluid retention in the lungs, where the patient needs medication. Acoustic signals from the heart and the lung were analyzed using wavelet transforms to measure changes in the CHF patient's status from the decompensated to compensated and vice versa. Measurements were taken on CHF patients diagnosed to be in compensated and decompensated states by using a digital stethoscope and electrocardiogram (ECG) in order to monitor their progress in the management of their disease. Analysis of acoustic signals of the heart due to the opening and closing of heart valves as well as the acoustic signals of the lungs due to respiration and the ECG signals are presented. Fourier, short-time Fourier, and wavelet transforms are evaluated to determine the best method to detect shifts in the status of a CHF patient. The power spectra obtained through the Fourier transform produced results that differentiate the signals from healthy people and CHF patients, while the short-time Fourier transform (STFT) technique did not provide the desired results. The most promising results were obtained by using wavelet analysis. Wavelet transforms provide better resolution, in time, for higher frequencies, and a better resolution, in frequency, for lower frequencies.

Improve quality of care with remote activity and fall detection using ultrasonic sensors.

In this paper a fall detection system is presented that automatically detects the fall of a person and their location using an array of ultrasonic wave transducers connected to a field-programmable gate array (FPGA) processor. Experimental results are provided on a prototype deployment installed at an assisted living community. The system can provide a cost-effective and intelligent method to help caregivers detect a fall quickly so that patients are treated in a timely manner. In addition to room monitoring and local alert functions, the system incorporates a personal computer and wireless connection to enable remote monitoring of patient's activity and health status.

Congestive Heart Failure home monitoring pilot study in urban Denver.

With a growing number of low-income patients developing Congestive Heart Failure in urban Denver, accessible and affordable solutions are needed to provide home management options. A multidisciplinary team evaluated currently available options for telemonitoring and developed a solution for an initial pilot study. This system is currently used in the Denver Metro area (Colorado) for 44 CHF patients. Preliminary results show this approach is effective and has reduced the patients' average length of stay at the hospital compared to historical data and control patients who do not use a remote monitoring system.

Lifetime estimation of wireless body area sensor network for patient health monitoring.

Wireless Body Area Sensor Networks (WBASN) is an emerging technology which utilizes wireless sensor nodes to implement real-time wearable health monitoring of patients to enhance independent living. These sensor nodes can be worn externally to monitor multiple bio-parameters (such as blood oxygen saturation (SpO(2)), blood pressure and heart activity) of multiple patients at a central location in the hospital. It is important to have an estimate of the time the first node will fail in order to replace or recharge the battery because the loss of critical data is not acceptable. Simulation is used to determine the lifetime of WBASN. The lifetime of the WBASN is defined as the duration of time until the first node fails due to battery depletion. In this paper, a parametric model of a health monitoring network (HMN) is created with sets of random input distributions. Probabilistic analysis is used to determine the timing and distribution of node failure in the HMN.