Extension of Tablo TrEatmeNt Duration (XTEND) study: successful 24 h prolonged therapy with Tablo in critical patients.

BACKGROUND: The Tablo® Hemodialysis System (Tablo) is an all in one, easy-to-learn device featuring integrated water purification, on demand dialysate production and two-way wireless data transmission and is approved for use in the acute, chronic, and home settings. Prior reports have demonstrated Tablo's ability to achieve clinical goals, seamlessly integrate into hospitals and reduce cost across a wide range of treatment times. Extension of the Tablo cartridge to 24 h allows prolonged therapy and even greater flexibility for prescribers in the acute setting. The objective is to report on the first ever experience with Tablo prolonged therapy between 12 and 24 h in critically ill patients treated at a single-center ICU. METHODS: Nursing staff were trained during a single training session on Tablo prolonged therapy. After a run-in period of five treatments, Tablo data were collected via real-time transmission to a cloud-based, HIPAA compliant platform and reviewed by site staff. Dialysis treatment delivery, clinically significant alarms, and clotting events were recorded. Sub-group analysis between COVID-19 positive and negative patients were reported. RESULTS: One hundred (100) consecutive Tablo prolonged treatments had a median prescribed treatment time of 24 h and a median achieved treatment time of 21.3 h. Median cartridge usage was 1.3 per treatment. The dialysis treatment time was delivered in 91% of treatments, with 6% ending early due to an alarm, and 3% ending due to clotting. Clinically significant alarms occurred at a median rate of 0.5 per treatment hour with a resolution time of 18 s. Median blood pump stoppage time related to these alarms was 2.3 min per treatment. Blood pump stoppage time was higher in the COVID-19 subgroup when compared to the non-COVID-19 subgroup. CONCLUSION: Tablo successfully achieves prescribed treatment time with minimal therapy interruptions from alarms or cartridge changes. This data demonstrates the effectiveness of Tablo in achieving personalization of treatments necessary for unstable patients and enabling successful delivery of extended therapy with minimal clotting. Tablo's prolonged therapy meets the needs of critically patients, including COVID-19 positive patients, requiring renal replacement therapy for greater than 12 h.

Jetson Nano and Arduino-Based Robot for Physical Distancing using Yolov4 Algorithm with Thermal Scanner

Coronavirus disease, more famously known as COVID-19, was first discovered in Wuhan, China;it was declared a global pandemic by WHO in March 2020. Due to the threatening characteristics of the virus, certain precautions had to be imposed by the government and health authorities to put the situation under control. To mitigate the further transmission of the virus, the "New Normal"was introduced to the public. This is by practicing the minimum safety protocols: wearing a facemask, frequently washing hands, and observing physical distancing. This study aims to build an autonomous robot that can monitor physical distancing, specifically focusing on people's queues. The robot utilizes the YOLOV4 Algorithm to detect the individuals and determine their Euclidean distance to determine if these people are observing the distance safety protocol that is 1.5 meters apart. The robot also includes a voice alarm that apprehends violators and reminds them to follow the practice. Moreover, the robot has an additional feature of detecting the body temperature of the people detected by the program. In assessing the robot's program, the implemented object detection achieved an accuracy of 93%, a precision of 87.5%, an error rate of 7%, and a recall of 94.6%. Moreover, by determining the constraint distance of the robot, which is 3.5 meters, the physical distancing program obtained a percent error of 4.26%. © 2022 IEEE.

Optimizing the Sensitivity of Biological Particle Detectors through Atmospheric Particle Analysis According to Climatic Characteristics in South Korea

Biological agents used in biological warfare or bioterrorism are also present in bioaerosols. Prompt identification of a biological weapon and its characteristics is necessary. Herein, we optimized an environmentally adaptive detection algorithm that can better reflect changes in the complex South Korean environment than the current models. The algorithm distinguished between normal and biological particles using a laser-induced fluorescence-based biological particle detector capable of real-time measurements and size classification. We ensured that the algorithm operated with minimal false alarms in any environment by training based on experimental data acquired from an area where rainfall, snow, fog and mist, Asian dust, and water waves on the beach occur. To prevent time and money wastage due to false alarms, the detection performance for each level of sensitivity was examined to enable the selection of multiple sensitivities according to the background, and the appropriate level of sensitivity for the climate was determined. The basic sensitivity was set more conservatively than before, with a 3% alarm rate at 20 agent-containing particles per liter of air (ACPLA) and a 100% alarm rate at 63 ACPLA. The reliability was increased by optimizing five variables. False alarms did not occur in situations where no alarm was unnecessary.

Fundamental limits on inferring epidemic resurgence in real time using effective reproduction numbers

We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.

Still Not Important Enough? COVID-19 Policy Views and Vote Choice

Scholars have long been skeptical of citizens' ability to vote on the basis of their policy views. Voters lack incentives to pay attention to politics and so are often unaware of the policy stances adopted by presidential candidates and parties. However, some scholars have suggested that voter attention may increase when policy issues become important to them, such as when a crisis disrupts their lives. The coronavirus pandemic provides an opportunity to test this proposition. It is one of the most severe crises the United States has faced. It has disrupted almost everyone's lives, and many people know someone who has tested positive or died from the virus. It is thus salient and important to many-if not most-voters. Despite this context, we find that many voters remain unaware of the 2020 US Presidential candidates' stances on coronavirus policies. Their levels of knowledge are about typical for other policies, which is middling. In the absence of knowledge, voters cannot connect their policy views on the virus with their presidential voting decisions.

Sensing and analysis of spectrum holes in ISM band using USRP testbed

The spectrum frequency in the wireless communication industry is getting great attention due to the internet of things (IoT) technology's growth. However, the radio spectrum's frequency band use is limited because the primary user for specific services can cause spectrum interference as multiple users use the same spectrum frequency. Meanwhile, at each spectrum frequency, the number of users and utilization time are distinct. These will create vacancies for spectrum frequency assigned to the primary user. A new alternative in using the cognitive radio (CR) spectrum is accessible to these vacancies. This paper analyzed the frequency spectrum in the industrial, scientific and medical (ISM) band and identified spectrum holes for transmission by the secondary users. This work employed a realistic approach by measuring the spectrum using Universal Software Radio Peripheral (USRP) devices. Thus, the spectrum holes in the frequency spectrum had distinguished by using an energy detection technique. In the energy detection technique, the threshold energy level is set and then compared with the energy detector output to identify the primary user's existence (PU). The result indicates that 0.61% of spectrum holes have been detected in the 2.43-2.44 GHz range. This range is sufficient for home appliances, radio frequency peripherals (RF), and bluetooth devices.

Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems.

Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed. The methodology is evaluated using MIMIC-III, an ICU patient research database. The results show that the proposed model can predict mortality within the ICU with AUROC values of 0.961, 0.936, 0.898, and 0.883 for age groups 18-45, 45-65, 65-85 and 85+, respectively. By using SHAP, the features with the highest impact in predicting mortality for different age groups and the threshold from which the value of a clinical feature has a negative impact on the patient's health can be identified. This allows ICU alarms to be improved by identifying the most important variables to be sensed and the threshold values at which the health personnel must be warned.

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions.

BACKGROUND: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients. OBJECTIVE: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies. METHODS: This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation. RESULTS: We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed). CONCLUSIONS: Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.