Improved ROSC rates in out-of-hospital cardiac arrest patients after introduction of a text message alert system for trained volunteers

Objective To evaluate whether a text message (TM) alert system for trained volunteers contributed to early cardiopulmonary resuscitation, the use of automated external defibrillators (AEDs), return of spontaneous circulation (ROSC) and survival in out-of-hospital cardiac arrest (OHCA) patients in a region with above-average survival rates. Design Data on all OHCA patients in 2012 (non-TM group) were compared with those of all OHCA patients in 2018 (TM group). The association of the presence of a TM alert system with ROSC and survival was assessed with multivariate regression analyses. Results TM responders reached 42 OHCA patients (15.9%) earlier than the first responders or ambulance. They connected 31 of these 42 OHCA patients (73.8%) to an AED before the ambulance arrived, leading to a higher percentage of AEDs being attached in 2018 compared to the 2012 non-TM group (55% vs 46%, p = 0.03). ROSC was achieved more often in the TM group (61.0% vs 29.4%, p < 0.01). Three-month and 1‑year survival did not differ significantly between the two groups (29.3% vs 24.3%, p = 0.19, and 25.9% vs 23.5%, p = 0.51). Multivariate regression analyses confirmed the positive association of ROSC with the TM alert system (odds ratio 1.49, 95% confidence interval 1.02‑2.19, p = 0.04). Conclusion A TM alert system seems to improve the chain of survival; because TM responders reached patients early, AEDs were attached more often and more OHCA patients achieved ROSC. However, the introduction of a TM alert system was not associated with improved 3‑month or 1‑year survival in a region with above-average survival rates.

FACE MASK DETECTION ALERT SYSTEM

In the present scenario due to Covid-19, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed. To identify the person on image/video stream wearing face mask or not. If the person doesn’t wear a mask, the notification will be sent to the respected admin with the help of Python and deep learning algorithm by using the Convolutional Neural Network, Keras Framework and OpenCV. Keywords: Computer Vision, Object Detection, Object Tracking, COVID-19, Face Masks, Safety Improvement

Blazar Variability with the Vera C. Rubin Legacy Survey of Space and Time

With their emission mainly coming from a relativistic jet pointing toward us, blazars are fundamental sources for studying extragalactic jets and their central engines, consisting of supermassive black holes fed by accretion disks. They are also candidate sources of high-energy neutrinos and cosmic rays. Because of the jet orientation, the nonthermal blazar emission is Doppler beamed; its variability is unpredictable, and it occurs on timescales from less than 1 hr to years. Comprehension of the diverse mechanisms producing the flux and spectral changes requires well-sampled multiband light curves over long time periods. In particular, outbursts are the best test bench for shedding light on the underlying physics, especially when studied in a multiwavelength context. The Vera C. Rubin Legacy Survey of Space and Time (Rubin-LSST) will monitor the southern sky for 10 yr in six photometric bands, offering a formidable tool for studying blazar variability features in a statistical way. The alert system will allow us to trigger follow-up observations of outstanding events, especially at high (keV-to-GeV) and very high (TeV) energies. We here examine the simulated Rubin-LSST survey strategies with the aim of understanding which cadences are more suitable for blazar variability science. Our metrics include light curve and color sampling. We also investigate the problem of saturation, which will affect the brightest and many flaring sources, and will have a detrimental impact on follow-up observations.

Clinical and economic impact of an alert system in primary care for the detection of patients with chronic hepatitis C

Introduction Prevalence of chronic hepatitis C (CHC) is higher in patients born between 1955–1975. The aim was to perform an economic evaluation of an age-based electronic health record (EHR) alert in primary care to detect patients with undiagnosed CHC and its treatment in comparison with non-use of the alert system, in Valencian Community, Spain. Materials and methods Decision trees and Markov model were used to evaluate the diagnosis and progression of the disease, respectively. CHC was diagnosed by serology and viral load in seropositive subjects. Epidemiological data and diagnostic costs were extracted from public sources of the Valencian Community. Probabilities, utilities and costs of model states were obtained from the literature. The impact on mortality and hepatic complications avoided by the implementation of the alert were estimated, and efficiency was measured as an incremental cost-utility ratio (ICUR) based on quality-adjusted life years (QALYs) and the costs of both alternatives. Results The EHR alert detected 269,548 patients, of whom 1,331 had CHC (vs. 23 patients with non-alert). Over the patients’ lifetime, the alert would prevent 93% of decompensated cirrhosis cases, 87% of hepatocellular carcinomas, 90% of liver transplants, and 89% of liver related deaths compared to non-use of the alert system. In addition, it would obtain an additional 3.3 QALY per patient, with an incremental cost of €10,880 and an ICUR of €3,321. Conclusions The implementation of an age-based EHR alert in primary care to detect patients with CHC reduces hepatic complications and mortality and is an efficient strategy.

Сomputer system for alerting indoor microclimate critical values

An alert system has been studied, in which sensor data can be collected and analyzed for further processing and action. Notifications are generated when data is compared against certain criteria. The ESP8266 microcontroller has been chosen as the basic device for implementing such a system, as this Espressif controller is a highly integrated Wi-Fi SoC solution that meets the demands of the cyberphysical systems industry in low power consumption, compactness and reliability. This solution is one of the most effective for the alert system. On the basis of this microcontroller the technical means of the computer system and the functional software for realization of functions of monitoring and the notification of critical values of a microclimate indoors are designed. The architecture of the computer system is studied. The choice of the main components of the hardware part of the system is substantiated, the element base is considered. A detailed scheme of the software operation algorithm is offered, as well as the results of the developed computer system for alerting the critical values of the indoor microclimate are demonstrated.