Lactate Dehydrogenase/Albumin To-Urea Ratio: A Novel Prognostic Maker for Fatal Clinical Complications in Patients with COVID-19 Infection.

Objective: To investigate lactate dehydrogenase/Albumin to-urea (LAU) ratio as a potential predictor for COVID-19-induced fatal clinical complications in hospitalized patients. Methods: This is a retrospective study involving blood analyses from 1139 hospitalised COVID-19 infection survivors and 349 deceased cases post-COVID-19 infection. Laboratory tests included complete blood picture, inflammatory markers, and routine organ function tests. Results: The non-survivor group showed lower haemoglobin (p < 0.001), platelet (p < 0.0001) and higher mean corpuscular volume, neutrophil count, neutrophil/lymphocytes ratio (NLR), and LAU (p < 0.001, p < 0.0013, p < 0.001, p < 0.0126) than the patients who survived the infection. The non-survivors also exhibited higher markers for infection-related clinical complications, such as international normalized ratio (INR), D-dimer, urea, total bilirubin, alkaline phosphatase (ALK), creatinine, c-reactive protein (CRP), and serum ferritin levels (all p < 0.05). In addition, LAU ratio was positively correlated with infection prognostic parameters including INR (r = 0.171), D-dimer (r = 0.176), serum urea (r = 0.424), total bilirubin (r = 0.107), ALK (r = 0.115), creatinine (r = 0.365), CRP (r = 0.268), ferritin (r = 0.385) and negatively correlated with serum albumin (r = −0.114) (p ≤ 0.05). LAU ratio had an area under receiver operating characteristic of 0.67 compared to 0.60 with NLR. Conclusion: Patients with a high LAU ratio are at increased risk of mortality due to COVID-19 infection. Therefore, early assessment of this parameter, intensive intervention and close monitoring could improve their prognosis.

An approach to a wrist wearable based Covid-19 prediction system to protect Health Care Professionals.

With healthcare professionals being the frontline warriors in battling the Covid pandemic, their risk of exposure to the virus is extremely high. We present our experience in building a system, aimed at monitoring the physiology of these professionals 24/7, with the hope of providing timely detection of infection and thereby better care. We discuss a machine learning approach and model using signals from a wrist wearable device to predict infection at a very early stage. In a double-blind test on a small group of patients, our model could successfully identify the infected versus non-infected cases with near 100% accuracy. We also discuss some of the challenges we faced, both technical and non-technical, to get this system off the ground as well as offer some suggestions that could help tackle these hurdles.

Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.

Antecedents of Self-Disclosure on Social Networking Sites (SNSs): A Study of Facebook Users

Self-disclosure on social networking sites (SNSs) leads to social capital development, connectedness, and relationship building. Due to several benefits associated with this behavior, self-disclosure has become a subject of research over the last few years. The current study investigates the antecedents of self-disclosure under the lens of the technology acceptance model (TAM). The research is quantitative, and the data were collected from 400 Pakistani Facebook users with a variety of demographic characteristics. The partial least squares-structural equation model (PLS-SEM) analysis technique was employed to analyze the data. The study′s findings confirmed that perceived usefulness is a strong predictor of personal information sharing, and it along with other variables causes a 31% variation in self-disclosure behavior. However, trust (medium and social) mediates the relationshipof perceived usefulness, privacy concerns, and self-disclosure behavior.

Bibliometric analysis of coronavirus disease (COVID-19) literature published in Web of Science 2019-2020.

Coronavirus outbreak in Wuhan, China, turned into a pandemic in record time. Communication of disease presentation and mechanism of spread remain keys to getting ahead of the virus and limiting its spread beyond the capacity of management. Owing to huge academic focus and pandemic concern around the globe, this bibliometric analysis investigated research productivity related to coronavirus disease (COVID-19) pandemic using the Web of Science database. The relevant data were harvested, and search query was further refined by publication years (2020 OR 2019) and document types (article, book chapter, and proceedings paper). Finally, 6694 records were imported and downloaded in Plaintext and BibTeX formats on August 1, 2020. The data analysis was performed using MS Excel, VOS viewer, and Biblioshiny software. Of the 6694 publications that appeared in that period, the USA and Chinese research institutions topped the numbers. At the same time, the Journal of Medical Virology and CUREUS (Cureus Journal of Medical Science), remained favorite journals for publications. The pattern of multi-author publications has outstripped that of single-authors. Apart from COVID-19 and the novel coronavirus, the important keywords mentioned included pandemic, pneumonia, epidemiology, public health, outbreak, epidemic, China, infection, and treatment. The analysis shows a strong local research response from China, with large teams reporting on the disease outbreak. Subsequent studies will document a global response as the virus spreads worldwide. The initial research related to the current coronavirus outbreak was reported from within China. The data and patterns were supposed to alter as the virus spread globally.

Bibliometric Analysis of Wastewater Literature Published in Web of Science 2019 to 2020

The present study used bibliometric and visualization techniques to analyze wastewater literature published in the Web of Science 2019-2020. The bibliometrix tool based on R package, Excel, MS-Access, ScientoPy, and VOS-viewer software packages were used for data analysis and bibliometric indicators extraction. This is for evaluating the research productivity of wastewater based on the data collected from documents that covered two recent years. The work ventured to examine wastewater researchers' overall performance in their research quest, productivity achievements, and publication accomplishments. The study answered questions related to most productive countries, organizations, and authors;preferred types of researcher's sources;authorship collaboration;most frequently used keyword and co-occurrence network in wastewater research;and influential research's citations and usage. Likewise, focus concentrated on top-ranked publications, authors per document, degree of collaboration based on the data collected.