Impact of Steam Inhalation, Saline Gargling, and Povidone-Iodine Gargling on Clinical Outcome of COVID-19 Patients in Bengaluru, Karnataka: A Randomized Control Trial.

Context: In the absence of any specific treatment available for COVID-19, people started practicing traditional nonpharmacological preventive home remedies such as salt water gargling and steam inhalation. The available research evidence on some of these measures opines that steam inhalation, saline gargling, and povidone-iodine gargling does have virucidal properties and do provide symptomatic relief. Aims: The aim is to test this hypothesis, and the present trial was undertaken with an objective to assess the effect of steam inhalation, saline gargling, and povidone-iodine gargling among the COVID-19-positive patients with respect to early test negativity and clinical recovery. Methodology: Open-labeled, parallel, randomized controlled trial was conducted among asymptomatic or mild COVID-19-positive patients in Bangalore from September 2020 to February 2021. In each group of steam inhalation, saline gargling, povidone-iodine gargling, and control, twenty participants were allocated. Daily follow-up was done for 21 days to assess early test negativity and clinical recovery. Trial Registry Number: Clinical Trial Registry India/2020/09/027687. Results: Among 80 participants recruited, 65 (81.3%) were symptomatic. Early test negativity was seen in povidone-iodine gargling group of 6 days (KaplanMeier survival curve, BreslowGeneralized Wilcoxon test P = 0.7 as per the intention-to-treat and as per-protocol P = 0.8). Significant clinical recovery was seen in saline gargling group (4 days, P = 0.01). Conclusion: Povidone-iodine gargling was effective in providing early test negativity, whereas saline gargling was effective in early clinical recovery.

A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data.

Medical data processing is exponentially increasing day by day due to the frequent demand for many applications. Healthcare data is one such field, which is dynamically growing day by day. In today's scenario, an enormous amount of sensing devices and data collection units have been employed to generate and collect medical data all over the world. These healthcare devices will result in big real-time data streams. Hence, healthcare-based big data analytics and monitoring have gained hawk-eye importance but needs improvisation. Recently, machine and deep learning algorithms have gained importance to analyze huge amounts of medical data, extract the information, and even predict the future insights of diseases and also cope with the huge volume of data. But applying the learning models to handle big/medical data streams remains to be a challenge among the researchers. This paper proposes the novel deep learning electronic record search engine algorithm (ERSEA) along with firefly optimized long short-term memory (LSTM) model for better data analytics and monitoring. The experimentations have been carried out using Apache Spark using the different medical respiratory data. Finally, the proposed framework results are contrasted with existing models. It shows the accuracy, sensitivity, and specificity like 94%, 93.5%, and 94% for less than 5 GB dataset, and also, more than 5 GB it provides 94%, 92%, and 93% to prove the extraordinary performance of the proposed framework.

A Versatile and Ubiquitous IoT-Based Smart Metabolic and Immune Monitoring System.

In the present medical age, the focus on prevention and prediction is achieved using the medical internet of things. With a broad and complete framework, effective behavioral, environmental, and physiological criteria are necessary to govern the major healthcare sectors. Wearables play an essential role in personal health monitoring data measurement and processing. We wish to design a variable and flexible frame for broad parameter monitoring in accordance with the convenient mode of wearability. In this study, an innovative prototype with a handle and a modular IoT portal is designed for environmental surveillance. The prototype examines the most significant parameters of the surroundings. This strategy allows a bidirectional link between end users and medicine via the IoT gateway as an intermediate portal for users with IoT servers in real time. In addition, the doctor may configure the necessary parameters of measurements via the IoT portal and switch the sensors on the wearables as a real-time observer for the patient. Thus, based on goal analysis, patient situation, specifications, and requests, medications may define setup criteria for calculation. With regard to privacy, power use, and computation delays, we established this system's performance link for three common IoT healthcare circumstances. The simulation results show that this technique may minimize processing time by 25.34%, save energy level up to 72.25%, and boost the privacy level of the IoT medical device to 17.25% compared to the benchmark system.