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Introduction: Albumin is the most abundant protein in serum and serves as a major transporter for many molecules. In maintaining homeostasis, albumin acts as a potential scavenger, antioxidant, and immunomodulator. As a transporter as well as an immunomodulator, the role of albumin and its correlation with anti-SARS-CoV-2 antibodies has not been widely studied. This study aims to measure the levels of albumin and IgG anti-SARS-CoV-2 N and S1 RBD proteins and determine whether there is a correlation between the two items. Materials and Methods: We conducted a cross-sectional study on 69 healthy adults aged 20–45 years who were fully vaccinated with SinoVac and then assessed the albumin and human IgG protein levels of SARS-CoV-2 N and S1 RBD proteins one month after the second dose using an ELISA method. Result: The average albumin level was 91.401 ng/mL and IgG was 11.419 Units/mL. Statistical calculations using the Spearman's rho correlation test got a significance value of 0.001 (p < 0.05) and the coefficient of correlation (r) value is 0.407, which means that there is a significant correlation between albumin and IgG anti-SARS-CoV-2 in people who were fully vaccinated with SinoVac, with a moderate coefficient of correlation. Conclusion: One of the immunomodulatory effects of albumin is to increase the expression of the pro-inflammatory gene, TNF-α, through the Toll-like receptor (TLR)-4 and NF-κB pathways. The presence of the TNF superfamily plays a role in the development, maturation, activation, and differentiation of B cells, which in turn produce a sufficient proportion of immunoglobulins. © 2023 UPM Press. All rights reserved.
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Purpose: COVID-19, government regulation and social media have changed many aspects of life including consumption behavior. The influence of social media by spreading massive news about COVID-19 also have the impact toward emotions threat, perceived risk, and perceived value of consumption behavior in Bengkulu Province, Indonesia. This research aims to analyze the extent to which changes in the consumption behavior, mainly for purchasing of corona prevention products through indirect distribution channels. Research design, data and methodology: This research was descriptive quantitative using the IPA method with sample of people in Bengkulu Province. This study obtained 208 respondent data from questionnaires and tested the validity and reliability with corrected-item total correlation method. Results: The study found that COVID-19 pandemic and government regulation variable were in quadrant II, in quadrant III there were social media variable, emotions threat and perceived risk. Meanwhile, in quadrant IV there were perceived value variable. Conclusions: The findings indicated that COVID-19 and government regulation are the most variable that influence people to buy corona prevention product, meanwhile perceived value is the less influence variable. Therefore, government and marketers have to prepare strategic plan in order to raise people awareness to avoid corona by buying corona prevention product. © Copyright: The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://Creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Emerging hardware tailored for artificial intelligence (AI) and machine learning (ML) methods provide novel means to couple them with traditional high performance computing (HPC) workflows involving molecular dynamics (MD) simulations. We propose Stream-AI-MD, a novel instance of applying deep learning methods to drive adaptive MD simulation campaigns in a streaming manner. We leverage the ability to run ensemble MD simulations on GPU clusters, while the data from atomistic MD simulations are streamed continuously to AI/ML approaches to guide the conformational search in a biophysically meaningful manner on a wafer-scale AI accelerator. We demonstrate the efficacy of Stream-AI-MD simulations for two scientific use-cases: (1) folding a small prototypical protein, namely ββα-fold (BBA) FSD-EY and (2) understanding protein-protein interaction (PPI) within the SARS-CoV-2 proteome between two proteins, nsp16 and nsp10. We show that Stream-AI-MD simulations can improve time-to-solution by ~50X for BBA protein folding. Further, we also discuss performance trade-offs involved in implementing AI-coupled HPC workflows on heterogeneous computing architectures. © 2021 ACM.
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The novel coronavirus SARS CoV-2 (COVID-19) is a pandemic disease and became a public health emergency worldwide. In the present study, comparison among most vigorously affected select cities across different countries, Delhi (India), Madrid (Spain), Lombardy (Italy), and New York and New Jersey (USA), were carried out up to July 2020. Predictive modelling was employed by using machine learning algorithms to predict the spread of the virus during August to November 2020. The results indicated that population density and urban density have a stronger connection with the spread of COVID-19 across the cities of Madrid, New York, and New Jersey. Conversely, Milan has exhibited a higher infection rate despite low population density (420 persons/km2) because of delayed human-human transmission measures and lockdowns. Relatively lower infection was recorded in Delhi even with higher population density (11,312 persons/km2) and higher urban compactness, which can be attributed to timely lockdowns and social distancing measures. The temperature and humidity have also abetted the spread of virus, especially in temperate regions with threshold temperature <10 °C with a humidity level between 60 and 77 g/m3. Predictive modelling reveals withdrawal of the pandemic across select cities by the end of 2020. The above findings would assist policymakers in making appropriate decisions for preventing the spread of this novel virus. © Springer Nature Switzerland AG 2021.
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BACKGROUND: It is nearly impossible to overestimate the burden of chronic pain, which is associated with enormous personal and socioeconomic costs. Chronic pain is the leading cause of disability in the world, is associated with multiple psychiatric comorbidities, and has been causally linked to the opioid crisis. Access to pain treatment has been called a fundamental human right by numerous organizations. The current COVID-19 pandemic has strained medical resources, creating a dilemma for physicians charged with the responsibility to limit spread of the contagion and to treat the patients they are entrusted to care for. METHODS: To address these issues, an expert panel was convened that included pain management experts from the military, Veterans Health Administration, and academia. Endorsement from stakeholder societies was sought upon completion of the document within a one-week period. RESULTS: In these guidelines, we provide a framework for pain practitioners and institutions to balance the often-conflicting goals of risk mitigation for health care providers, risk mitigation for patients, conservation of resources, and access to pain management services. Specific issues discussed include general and intervention-specific risk mitigation, patient flow issues and staffing plans, telemedicine options, triaging recommendations, strategies to reduce psychological sequelae in health care providers, and resource utilization. CONCLUSIONS: The COVID-19 public health crisis has strained health care systems, creating a conundrum for patients, pain medicine practitioners, hospital leaders, and regulatory officials. Although this document provides a framework for pain management services, systems-wide and individual decisions must take into account clinical considerations, regional health conditions, government and hospital directives, resource availability, and the welfare of health care providers.