Evaluation of oxidative stress parameters and metabolic activities of nurses working day and night shifts

The aim of this study was to evaluate the oxidative stress and metabolic activities of nurses working day and night shifts. Intensive care unit (ICU) (n=70) and ordinary service (OS) nurses (n=70) were enrolled in the study. Just before and the end of the shifts, blood samples were obtained to measure the participants' oxidative stress parameters. Metabolic activities were analyzed using the SenseWear Armband. Oxidative stress parameters were increased at the end of the shifts for all OS and ICU nurses compared to the beginning of the shifts. Compared to the OS nurses, the ICU nurses' TAS, TOS, and OSI levels were not significantly different at the end of the day and night shifts. The metabolic activities of the OS and ICU nurses were found to be similar. As a result, the OS and ICU nurses' oxidative stress parameters and metabolic activities were not different, and all of the nurses experienced similar effects from both the day and night shifts

Cross-Country Growth Empirics and Model Uncertainty: An Overview

The aim of this paper is to provide an overview of empirical cross-country growth literature. The paper begins with describing the basic framework used in recent empirical cross-country growth research. Even though this literature was mainly inspired by endogenous growth theories, the neoclassical growth model is still the workhorse for cross-country growth empirics. The second part of the paper emphasises model uncertainty, which is indeed immense but generally neglected in the empirical cross-country growth literature. The most outstanding feature of the literature is that a large number of factors have been suggested as fundamental growth determinants. Together with the small sample property, this leads to an important problem: model uncertainty. The questions which factors are more fundamental in explaining growth dynamics and hence growth differences are still the subject of academic research. Recent attempts based on general-to-specific modeling or model averaging are promising but have their own limits. Finally, the paper highlights the implications of model uncertainty for policy evaluation

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

AbstractChest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.</jats:p

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls