Interpreting High Sensitivity Troponins in Various Acute Clinical Settings - Increased Significance to the Present.Day Cardiologist

High-sensitivity cardiac troponins expedite the evaluation of patients with chest pain in the emergency department. The utility of troponins extends beyond the acute coronary syndromes to accurate the diagnosis of myocardial injury. Troponins are best friends for physicians;however, they are a double-edged sword if not interpreted appropriately. Misdiagnosis is harmful with regard to patient outcomes. The present review focuses on the recent updates in the understanding and interpretation of high-sensitivity troponins in various acute clinical settings. Common mistakes and gray zones in the interpretation of troponins, the concept of myocardial injury versus infarction, newer entities like myocardial infarction (MI) with Nonobstructive Coronary Arteries, recent controversies over the definition of periprocedural MI, complementary role of imaging in the diagnosis of myocardial injury and the role of troponins in the current COVID-19 pandemic are discussed.Copyright © 2022 Saudi Center for Organ Transplantation.

A portable light scattering system for detecting expelled droplets to assess Covid-19 transmission risk

The COVID-19 pandemic has caused a marked disruption in the delivery of medical care, resulting in significant negative consequences for patients. Considering Covid-19 spreads primarily through expelled respiratory droplets, the ability to detect and measure droplets is critical to the development of clinical protective practices. However, most available methods are either unsuitable for the clinical setting, or cannot distinguish solid particles from liquid droplets. We developed a robust and portable optical instrument capable of measuring the size and quantity of droplets generated during medical procedures. Here we outline the system design and describe our preclinical measurements, which showed that surgical masks significantly reduce the number of expelled speech droplets. Copyright © 2022 SPIE.

Human Factors in the Adoption of M-Learning by COVID-19 Frontline Learners

Mobile Learning is crucial to the continuity of healthcare education during COVID-19. Despite its penchant for the traditional delivery of course content through classroom and clinical settings, M-Learning proved to be a viable solution in a pandemic due to social isolation, community restrictions, and safety concerns. We invited 219 frontline learners from 3 universities, active healthcare professionals who are currently enrolled, to test a structural model based on the Theory of Reason Action. We positioned the human factors of cognitive, social, and affective needs as determinants of attitude in the behavioral intention to adopt M-Learning. We further hypothesize that social norms positively influence the behavioral intention to adopt M-Learning among healthcare frontliners. We applied PLS-SEM to analyze the survey data and revealed that human factors positively influence attitude, leading to the behavioral intention to adopt M-Learning. Social norms and their influence on the behavioral intention to adopt this technology are not supported. We discuss the implications of our study, acknowledge its limitations while mapping out directions for future works to understand M-Learning adoption further. © 2021 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings. All rights reserved

Predicting Covid-19 using Random Forest Machine Learning Algorithm

COVID-19, also known as 2019-nCoV, is no longer a pandemic but an endemic disease that has killed many people worldwide. COVID-19 has no precise treatment or remedy at this time, but it is unavoidable to live with the disease and its implications. By quickly and efficiently screening for covid, one may determine whether or not one has COVID-19 and thus limit the financial and administrative burdens on healthcare systems. Research has shown that predictions which use many variables in order to predict the likelihood of infection have been established. Due to the world's inadequate healthcare systems, this fact places significant strain on these countries' healthcare systems, particularly in emerging nations. While there is no proven antiviral medication method or licensed vaccine that can eliminate the COVID-19 pandemic, there are other potential options that would alleviate both healthcare systems and the economy from the weight of the virus. Non-clinical approaches like machine learning, data mining, deep learning, and other artificial intelligence approaches are among the most promising approaches for use outside of a clinical setting. To make diagnosis and prognosis for patients with the 2019-NCoV pandemic easier, use these options. Additionally, artificial intelligence systems, such as decision trees, support vector machines, artificial neural networks, and naïve Bayesian models, are validated using a positive and negative COVID-19 case dataset. To establish the degree of connection between dependent characteristics, correlation coefficients between different dependent and independent variables were investigated. During preparation, the model was trained for 80% of the time, while at the same time, it was tested for 20% of the time. Based on the success evaluation, the Random Forest had the best precision of 94.99%. © 2021 IEEE.

An Emerging Preventive Mental Health Care Strategy: The Neurobiological and Functional Basis of Positive Psychological Traits.

Even with the expanding burden of the COVID-19 pandemic on mental health, our approach to mental health care remains largely reactive rather than preventive. This trend is problematic because the majority of outpatient visits to primary care providers across the country is related to unmet mental health needs. Positive psychology has the potential to address these issues within mental health care and provide primary care providers with strategies to serve their patients more effectively. Positive psychology has many frameworks like hope, which can be measured using simple questionnaires in the waiting room. Moreover, there is a growing body of neurobiological evidence that lends credence to positive psychology concepts in the context of differential neuronal activation patterns. Many positive psychological instruments not only have high construct validity but also have connections to observable neurobiological differences tied to differences in psychosocial functioning. Despite the current evidence, we still need robust research that explores if such psychometric measurements and related interventions lead to clinically significant and favorable health outcomes in patients outside of controlled environments.