Relationship between thyroid function tests and mortality in severe Covid-19 pneumonia cases admitted to the intensive care unit Severe COVID-19 and thyroid function tests

Aim: In severe COVID-19 infection, most organs are affected, including the thyroid gland. A decrease in thyroid functions can be seen in relation to the severity of the disease. We aimed to retrospectively analyze the relationship between thyroid function tests and mortality in patients admitted to the intensive care unit (ICU) with severe COVID-19 pneumonia. Material(s) and Method(s): The study was performed retrospectively on 46 adult patients admitted to the intensive care unit with severe COVID-19 pneumonia. Demographic, clinical, laboratory data were recorded. Patients were grouped into two according to mortality. Laboratory data were compared between groups. Additionally, the correlation of free triiodothyronine (fT3), free thyroxine (fT4), and thyrotropin (TSH) with infection parameters was investigated. Result(s): At the time of ICU admission, fT3 levels below the normal range were present in 91.3%, fT4 levels were below normal in 39.13%, and TSH levels were below normal in 52.17% of the study patients. There was a positive correlation between fT4 and CRP (r=0.315, p<0.05), while there were no significant correlations between other parameters. TSH, fT3, or fT4 did not differ between patients with and without mortality. Partial arterial oxygen pressure/fraction of inspired oxygen was lower in patients with mortality (p=0.015). Discussion(s): Low thyroid hormone levels and TSH are common occurrences in patients admitted to the ICU with severe COVID-19 pneumonia. No relationship could be shown between low thyroid function test levels and mortality in patients with severe COVID-19 pneumonia.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic?

The concept of Artificial Intelligence (AI), born as the possibility of simulating the human brain's learning capabilities, quickly evolves into one of the educational technology concepts that provide tools for students to better themselves in a plethora of areas. Unlike the previous educational technology iterations, which are limited to instrumental use for providing platforms to build learning applications, AI has proposed a unique education laboratory by enabling students to explore an instrument that functions as a dynamic system of computational concepts. However, the extent of the implications of AI adaptation in modern education is yet to be explored. Motivated to fill the literature gap and to consider the emerging significance of AI in education, this paper aims to analyze the possible intertwined relationship between students' intrinsic motivation for learning Artificial Intelligence during the COVID-19 pandemic;the relationship between students' computational thinking and understanding of AI concepts;and the underlying dynamic relation, if existing, between AI and computational thinking building efforts. To investigate the mentioned relationships, the present empirical study employs mediation analysis based upon collected 137 survey data from Universidad Politécnica de Madrid students in the Institute for Educational Science and the School of Naval Architecture and Marine Engineering during the first quarter of 2022. Findings show that intrinsic motivation mediates the relationship between perceived Artificial Intelligence learning and computational thinking. Also, the research indicates that intrinsic motivation has a significant relationship with computational thinking and perceived Artificial Intelligence learning. © 2023

The predictive role of Troponin I levels for mortality in geriatric patients transferred to the intensive care unit for COVID-19 pneumonia

Aim: Troponin I is an important prognostic marker in critically ill patients with COVID-19, similar to cytokines and other inflammatory mediators. The aim of this study was to evaluate the predictive value of troponin I levels for mortality in geriatric patients transferred to the intensive care unit for COVID-19 pneumonia according to age group. Material and Methods: Seventy-four patients with COVID-19 pneumonia were grouped according to age (Group 1:65-74 years, Group 2: 75-84 years, and Group 3: >= 85 years) and retrospectively analyzed. Demographics, clinical findings, laboratory results upon admission to the intensive care unit, and outcomes were compared among the groups. Predictive value of troponin I levels upon admission to intensive care unit (Troponin Iicu), difference in troponin levels between general wards and intensive care unit (Troponin Idiff), C-reactive protein, ferritin, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, procalcitonin, and D-dimer levels for mortality were also investigated. Results: The mortality rate was 74.3% for the patients overall, and increased, albeit insignificantly, with increasing age. Neither Troponin Iicu nor Troponin Idiff was predictive for mortality for any of the age groups or for the patients overall. Ferritin, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, and C-reactive protein levels were predictive for mortality for patients overall (p= 0.016, p= 0.001, p= 0.013, and p < 0.001, respectively). Discussion: For geriatric patients, troponin I levels at the time of the first admission to the ICU are not sufficient to predict mortality alone and should be evaluated together with other parameters.

Government ICT-based cross sectoral collaboration strategy for financial support of low-income families during the COVID-19 pandemic

In the time of the COVID-19 crisis, information and communication technologies (ICTs) have gained more attention, and they are now an important instrument of governmental policies and decision-making processes. The objective of this paper is to explore the application of the Pakistani government's ICT-based cross-sectoral collaboration strategy during the COVID-19 pandemic. The narrative literature review and case study approaches are employed for this study. After the COVID-19 breakout, the government of Pakistan immediately took initiative and launched a financial support program for low income people known as the "Ehsaas emergency cash"program. This study examines the government's cross-sectoral collaboration strategy, which was undertaken to help and support low-income through mobile payments and also promote public-private collaboration. The government policy also helps to maintain social distance and ensures transparency in the distribution of funds under the Ehsaas emergency cash program. © 2020 ACM.

Public-Private ICT-Based Collaboration Initiative During the COVID-19 Pandemic: The Case of Ehsaas Emergency Cash Program in Pakistan

The purpose of this study is to explore the application of the Pakistani government's ICT-based public-private collaboration strategy during the COVID-19 pandemic. This investigation covers the Ehsaas Emergency Cash (EEC) program, from the beginning of the application to receiving of funds, which was developed by a collaborative mechanism involving the Pakistani government, National Database and Registration Authority (NADRA), State Bank of Pakistan (SBP), local commercial banks and the telecom sector. By investigating the EEC via the case study method, this paper provides insights to policymakers on how to replicate similar strategies in various country contexts via leveraging public-private collaboration, ICT technologies, and policymaking. This study contributes the COVID-19, ICT, e-government, and public-private collaboration literature via providing insight about current events and a model based on the case presented.