Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach.

Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient's cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

Associations of Immunogenicity and Reactogenicity After SARS-CoV-2 mRNA-1273 Vaccine in COVE and TeenCOVE Trials.

BACKGROUND: The reactogenicity and immunogenicity of Coronavirus 2019 (COVID-19) vaccines is well-studied. Little is known regarding the relationship between immunogenicity and reactogenicity of COVID-19 vaccines. METHODS: This study assessed the association between immunogenicity and reactogenicity after two mRNA-1273 (100 µg) injections in 1671 total adolescent and adult participants (≥12 years) from the primary immunogenicity sets of the blinded periods of the Coronavirus Efficacy (COVE) and TeenCOVE trials. Associations between immunogenicity through day 57 and solicited ARs after the first and second injections of mRNA-1273 were evaluated among participants with and without solicited ARs using linear mixed-effects models. RESULTS: The mRNA-1273 reactogenicity in this combined analysis set was similar to that reported for these trials. The vaccine elicited high neutralizing antibody (nAb) geometric mean titers (GMTs) in evaluable participants. GMTs at day 57 were significantly higher in participants who experienced solicited systemic ARs after the second injection (1227.2 [1164.4-1293.5]) than those who did not (980.1 [886.8-1083.2], p = 0.001) and were associated with fever, chills, headache, fatigue, myalgia, and arthralgia. Significant associations with local ARs were not found. CONCLUSIONS: These data show an association of systemic ARs with increased nAb titers following a second mRNA-1273 injection. While these data indicate systemic ARs are associated with increased antibody titers, high nAb titers were observed in participants after both injections, consistent with the immunogenicity and efficacy in these trials. These results add to the body of evidence regarding the relationship of immunogenicity and reactogenicity and can contribute toward the design of future mRNA vaccines.

A Web Knowledge-Driven Multimodal Retrieval Method in Computational Social Systems: Unsupervised and Robust Graph Convolutional Hashing

Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs;2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit;and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, called unsupervised and robust graph convolutional hashing (URGCH). The specific implementations are as follows: first, a "secondary semantic self-fusion" approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking;second, a "adaptive computing" approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features;Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about 1%-3.7% in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS.

Evaluation of mRNA-1273 Vaccine in Children 6 Months to 5 Years of Age.

BACKGROUND: The safety, reactogenicity, immunogenicity, and efficacy of the mRNA-1273 coronavirus disease 2019 (Covid-19) vaccine in young children are unknown. METHODS: Part 1 of this ongoing phase 2-3 trial was open label for dose selection; part 2 was an observer-blinded, placebo-controlled evaluation of the selected dose. In part 2, we randomly assigned young children (6 months to 5 years of age) in a 3:1 ratio to receive two 25-µg injections of mRNA-1273 or placebo, administered 28 days apart. The primary objectives were to evaluate the safety and reactogenicity of the vaccine and to determine whether the immune response in these children was noninferior to that in young adults (18 to 25 years of age) in a related phase 3 trial. Secondary objectives were to determine the incidences of Covid-19 and severe acute respiratory syndrome coronavirus 2 infection after administration of mRNA-1273 or placebo. RESULTS: On the basis of safety and immunogenicity results in part 1 of the trial, the 25-µg dose was evaluated in part 2. In part 2, 3040 children 2 to 5 years of age and 1762 children 6 to 23 months of age were randomly assigned to receive two 25-µg injections of mRNA-1273; 1008 children 2 to 5 years of age and 593 children 6 to 23 months of age were randomly assigned to receive placebo. The median duration of follow-up after the second injection was 71 days in the 2-to-5-year-old cohort and 68 days in the 6-to-23-month-old cohort. Adverse events were mainly low-grade and transient, and no new safety concerns were identified. At day 57, neutralizing antibody geometric mean concentrations were 1410 (95% confidence interval [CI], 1272 to 1563) among 2-to-5-year-olds and 1781 (95% CI, 1616 to 1962) among 6-to-23-month-olds, as compared with 1391 (95% CI, 1263 to 1531) among young adults, who had received 100-µg injections of mRNA-1273, findings that met the noninferiority criteria for immune responses for both age cohorts. The estimated vaccine efficacy against Covid-19 was 36.8% (95% CI, 12.5 to 54.0) among 2-to-5-year-olds and 50.6% (95% CI, 21.4 to 68.6) among 6-to-23-month-olds, at a time when B.1.1.529 (omicron) was the predominant circulating variant. CONCLUSIONS: Two 25-µg doses of the mRNA-1273 vaccine were found to be safe in children 6 months to 5 years of age and elicited immune responses that were noninferior to those in young adults. (Funded by the Biomedical Advanced Research and Development Authority and National Institute of Allergy and Infectious Diseases; KidCOVE ClinicalTrials.gov number, NCT04796896.).

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19☆

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

Interleukin-6 cytokine: An overview of the immune regulation, immune dysregulation, and therapeutic approach.

Several studies have shown that interleukin 6 (IL-6) is a multifunctional cytokine with both pro-inflammatory and anti-inflammatory activity, depending on the immune response context. Macrophages are among several cells that secrete IL-6, which they express upon activation by antigens, subsequently inducing fever and production of acute-phase proteins from the liver. Moreover, IL-6 induces the final maturation of B cells into memory B cells and plasma cells as well as an adaptive role for short-term energy allocation. Activation of IL-6 receptors results in the intracellular activation of the JAK/STAT pathway with resultant production of inflammatory cytokines. Several mechanisms-controlled IL-6 expression, but aberrant production was shown to be crucial in the pathogenesis of many diseases, which include autoimmune and chronic inflammatory diseases. IL-6 in combination with transforming growth factor ß (TGF-ß) induced differentiation of naïve T cells to Th17 cells, which is the cornerstone in autoimmune diseases. Recently, IL-6 secretion was shown to form the backbone of hypercytokinemia seen in the Coronavirus disease 2019 (COVID-19)-associated hyperinflammation and multiorgan failure. There are two classes of approved IL-6 inhibitors: anti-IL-6 receptor monoclonal antibodies (e.g., tocilizumab) and anti-IL-6 monoclonal antibodies (i.e., siltuximab). These drugs have been evaluated in patients with rheumatoid arthritis, juvenile idiopathic arthritis, cytokine release syndrome, and COVID-19 who have systemic inflammation. JAK/STAT pathway blockers were also successfully used in dampening IL-6 signal transduction. A better understanding of different mechanisms that modulate IL-6 expression will provide the much-needed solution with excellent safety and efficacy profiles for the treatment of autoimmune and inflammatory diseases in which IL-6 derives their pathogenesis.

Did the Islamic Stock Index Provide Shelter for Investors during the COVID-19 Crisis? Evidence from an Emerging Stock Market

The economic and financial chaos caused by COVID-19 has been a discussion topic since the beginning of 2020. This study intends to provide a parallel comparison of volatility change and external shock persistence of the Islamic and conventional stock indexes of the Pakistan Stock Exchange. The daily stock index was extracted from Eikon Thomson Reuters for the conventional and Islamic stock index from Jan 2018 to April 2021, which was further divided in three periods, i.e., full, pre-, and post-pandemic period. The data have been analyzed using generalized autoregressive conditional heteroscedasticity (GARCH). An optimally parameterized GARCH (1,1) model is used to measure volatility change for both the pre- to post-pandemic periods. The results suggest that the magnitude of risk in a conventional index is significantly higher than that of the Islamic stock index for the period of study. However, the level of COVID shock persistence is longer in the KSE (conventional) index compared to the KMI (Islamic) index.

Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities

Demographic factors, statistical information, and technological innovation are prominent factors shaping energy transitions in the residential sector. Explaining these energy transitions requires combining insights from the disciplines investigating these factors. The existing literature is not consistent in identifying these factors, nor in proposing how they can be combined. In this paper, three contributions are made by combining the key demographic factors of households to estimate household energy consumption. Firstly, a mathematical formula is developed by considering the demographic determinants that influence energy consumption, such as the number of persons per household, median age, occupancy rate, households with children, and number of bedrooms per household. Secondly, a geographical position algorithm is proposed to identify the geographical locations of households. Thirdly, the derived formula is validated by collecting demographic factors of five statistical regions from local government databases, and then compared with the electricity consumption benchmarks provided by the energy regulators. The practical feasibility of the method is demonstrated by comparing the estimated energy consumption values with the electricity consumption benchmarks provided by energy regulators. The comparison results indicate that the error between the benchmark and estimated values for the five different regions is less than 8% (7.37%), proving the efficacy of this method in energy consumption estimation processes.

Evaluation of mRNA-1273 SARS-CoV-2 Vaccine in Adolescents.

BACKGROUND: The incidence of coronavirus disease 2019 (Covid-19) among adolescents between 12 and 17 years of age was approximately 900 per 100,000 population from April 1 through June 11, 2021. The safety, immunogenicity, and efficacy of the mRNA-1273 vaccine in adolescents are unknown. METHODS: In this ongoing phase 2-3, placebo-controlled trial, we randomly assigned healthy adolescents (12 to 17 years of age) in a 2:1 ratio to receive two injections of the mRNA-1273 vaccine (100 µg in each) or placebo, administered 28 days apart. The primary objectives were evaluation of the safety of mRNA-1273 in adolescents and the noninferiority of the immune response in adolescents as compared with that in young adults (18 to 25 years of age) in a phase 3 trial. Secondary objectives included the efficacy of mRNA-1273 in preventing Covid-19 or asymptomatic severe acute respiratory syndrome coronavirus 2 infection. RESULTS: A total of 3732 participants were randomly assigned to receive mRNA-1273 (2489 participants) or placebo (1243 participants). In the mRNA-1273 group, the most common solicited adverse reactions after the first or second injections were injection-site pain (in 93.1% and 92.4%, respectively), headache (in 44.6% and 70.2%, respectively), and fatigue (in 47.9% and 67.8%, respectively); in the placebo group, the most common solicited adverse reactions after the first or second injections were injection-site pain (in 34.8% or 30.3%, respectively), headache (in 38.5% and 30.2%, respectively), and fatigue (in 36.6% and 28.9%, respectively). No serious adverse events related to mRNA-1273 or placebo were noted. The geometric mean titer ratio of pseudovirus neutralizing antibody titers in adolescents relative to young adults was 1.08 (95% confidence interval [CI], 0.94 to 1.24), and the absolute difference in serologic response was 0.2 percentage points (95% CI, -1.8 to 2.4), which met the noninferiority criterion. No cases of Covid-19 with an onset of 14 days after the second injection were reported in the mRNA-1273 group, and four cases occurred in the placebo group. CONCLUSIONS: The mRNA-1273 vaccine had an acceptable safety profile in adolescents. The immune response was similar to that in young adults, and the vaccine was efficacious in preventing Covid-19. (Funded by Moderna and the Biomedical Advanced Research and Development Authority; Teen COVE ClinicalTrials.gov number, NCT04649151.).

The Strategic Role of Healthcare Professionals in the Battle Against COVID-19: Do We Really Need Help from Community Pharmacists?

Wuhan, the capital of China's Hubei province, was recognised in December 2019 as the centre of an outbreak of an unknown originator of pneumonia. Intense concentrations of illness spread throughout China and, ultimately, globally. Consequently, on 7th January, 2020, Chinese researchers identified a case of severe acute respiratory syndrome coronavirus (SARS-CoV-2) in an affected person in Wuhan as the cause. Symptoms of SARS-CoV-2 vary from mild (fever, dry cough, difficulty in breathing, and pain in muscles) to severe (acute respiratory distress syndrome [ARDS}, azotemia or acute renal failure, ventilation associated pneumonia [VAP], and shock from sepsis). In a multidisciplinary team, pharmacists play a strategic role as medical healthcare professionals in restricting the dissemination of SARS-CoV-2 and can serve as sentinels in their communities to control and counteract this epidemic domestically. Key Words: SARS-CoV-2, Community pharmacists, Frontline healthcare workers.

Prediction Models for COVID-19 Integrating Age Groups, Gender, and Underlying Conditions

The COVID-19 pandemic has caused hundreds of thousands of deaths, millions of infections worldwide, and the loss of trillions of dollars for many large economies. It poses a grave threat to the human population with an excessive number of patients constituting an unprecedented challenge with which health systems have to cope. Researchers from many domains have devised diverse approaches for the timely diagnosis of COVID-19 to facilitate medical responses. In the same vein, a wide variety of research studies have investigated underlying medical conditions for indicators suggesting the severity and mortality of, and role of age groups and gender on, the probability of COVID-19 infection. This study aimed to review, analyze, and critically appraise published works that report on various factors to explain their relationship with COVID-19. Such studies span a wide range, including descriptive analyses, ratio analyses, cohort, prospective and retrospective studies. Various studies that describe indicators to determine the probability of infection among the general population, as well as the risk factors associated with severe illness and mortality, are critically analyzed and these findings are discussed in detail. A comprehensive analysis was conducted on research studies that investigated the perceived differences in vulnerability of different age groups and genders to severe outcomes of COVID-19. Studies incorporating important demographic, health, and socioeconomic characteristics are highlighted to emphasize their importance. Predominantly, the lack of an appropriated dataset that contains demographic, personal health, and socioeconomic information implicates the efficacy and efficiency of the discussed methods. Results are overstated on the part of both exclusion of quarantined and patients with mild symptoms and inclusion of the data from hospitals where the majority of the cases are potentially ill.

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm.

Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.