Plos One
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Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. ⋯ Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.
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Coronavirus disease 2019 (COVID-19) remains a global challenge. Corticosteroids constitute a group of anti-inflammatory and immunosuppressive drugs that are widely used in the treatment of COVID-19. Comprehensive reviews investigating the comparative proportion and efficacy of corticosteroid use are scarce. Therefore, we conducted a systematic review and meta-analysis of clinical trials to evaluate the proportion and efficacy of corticosteroid use for the treatment of COVID-19. ⋯ The proportion of COVID-19 patients who received corticosteroids was significantly lower than that of patients who did not receive corticosteroids. Corticosteroid use in subjects with severe acute respiratory syndrome coronavirus 2 infections delayed viral clearance and did not convincingly improve survival; therefore, corticosteroids should be used with extreme caution in the treatment of COVID-19.
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In December 2019, a new disease named coronavirus disease 2019 (COVID-19) was occurred. Patients who are critically ill with COVID-19 are more likely to die, especially elderly patients. We aimed to describe the effect of age on the clinical and immune characteristics of critically ill patients with COVID-19. ⋯ A high proportion of critically ill patients were 60 or older. Furthermore, rapid disease progression was noted in elderly patients. Therefore, close monitoring and timely treatment should be performed in elderly COVID-19 patients.
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With the introduction of effective antiretroviral therapy (ART), people living with HIV (PLWH) are surviving longer and are at risk for developing metabolic abnormalities that contribute to cardiovascular disease (CVD). In Sub-Saharan Africa (SSA), there is a paucity of epidemiological data on lipid profiles among young adults receiving ART. This study aimed to estimate the prevalence of low high-density lipoprotein cholesterol (HDL-c), a cardioprotective lipid class, and whether it differed by age among adults on ART in Livingstone, Zambia. ⋯ Low HDL-c is highly prevalent among young adult with HIV in SSA independent of other risk factors for metabolic derangements. Lipid abnormalities among young PLWH may contribute to the early development of cardiovascular diseases in this population. This highlights the need to consider low HDL-c in the quest to reduce CVD risk among young adults on ART in SSA.
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This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. ⋯ On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.