Cardiovascular Diseases Risk Predictors: ABO Blood Groups in a Different Role.

Cardiovascular diseases (CVD) pose a serious threat to people's health, with extremely high global morbidity, mortality, and disability rates. This study aimed to review the literature that examined the relationship between blood groups and CVD. Many studies have reported that non-O blood groups are associated with an increased risk and severity of coronary artery disease (CAD) and acute coronary syndromes (ACS). Non-O blood groups increase the risk and severity of these conditions by increasing von-Willebrand factor (VWF) and plasma cholesterol levels and inducing endothelial dysfunction and inflammation. They have also been linked with increased coronary artery calcification, coronary lesion complexity, and poor collateral circulation. Blood groups also affect the prognosis of CAD and ACS and can alter the rate of complications and mortality. Several cardiovascular complications have been described for COVID-19, and blood groups can influence their occurrence. No studies have found a significant relationship between the Lewis blood group and CVD. In conclusion, people with non-O blood groups should be vigilantly monitored for cardiovascular risk factors as prevention and proper treatment of these risk factors may mitigate their risk of CVD and adverse cardiovascular events.

Lipids and diastolic dysfunction: Recent evidence and findings.

AIM: Diastolic dysfunction is the decreased flexibility of the left ventricle due to the impaired ability of the myocardium to relax and plays an important role in the pathogenesis of heart failure. Lipid metabolism is a well-known contributor to cardiac conditions, including ventricular function. In this article, we aimed to review the literature addressing the connections between lipids, their storage, and metabolism with left ventricular diastolic dysfunction. DATA SYNTHESIS: We searched Google scholar, Pubmed, Embase and Researchgate for our keywords: "Diastolic function", "Fat" and "Lipid profile". Initially, 250 articles were selected by title and 84 of them were chosen as most relevant and directly reviewed. CONCLUSIONS: Alterations of lipid metabolism in cardiac muscle and cardiac lipid content can occur in many conditions, including consumption of a high-fat diet, obesity, metabolic syndrome, and non-alcoholic fatty liver disease (NAFLD). These conditions induce alterations in myocardial lipid metabolism, increase myocardial fat content and epicardial fat thickness and increase inflammation and oxidative stress which ultimately lead to cardiac lipotoxicity and diastolic dysfunction. The effects of lipids on diastolic function can differ based on gender. Lipid profile and metabolism are as important in the pathogenesis of diastolic dysfunction as they are in other cardiovascular disorders. A more careful look at cardiac lipid metabolism in molecular, histological and gross levels results in more precise understanding of its role in myocardial function and leads to development of potential treatments for diastolic dysfunction.

Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study.

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94-98], specificity of 95% [90-99], positive predictive value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76-89], diagnostic odds ratio (DOR) of 496 [198-1,245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94-98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96-0.98] and 0.92 [0.91-0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.