Multivariate Analysis Approaches for Dimension and Shape Discrimination of Vitis vinifera Varieties.

In this study, berry dimensions and shape traits, which are important for the design of the grape processing system and the classification of 10 different grape varieties grown in same ecological conditions ('Ata Sarisi', 'Baris', 'Dimiski', 'Hatun Parmagi', 'Helvani', 'Horoz Karasi', 'Hönüsü', 'Italia', 'Mevlana Sarisi', and 'Red Globe') were determined; differences between the varieties were identified with the use of discriminant analysis. The largest grape varieties were identified as 'Ata Sarisi' and 'Red Globe'. The 'Red Globe' and 'Helvani' varieties had geometrically sphere-like shape. The 'Baris' variety had the lowest size averages. According to elliptic Fourier analysis, the primary source of shape variation was ellipse and sphere-looking varieties. However, shape variation was seen due to the existence of a small number of drop-like varieties. According to discriminant analysis, shape differences of the varieties were defined by two discriminant functions. Based on these discriminant functions, the greatest classification performance was achieved for 'Mevlana Sarisi' and 'Dimiski'. In scatter plots, three shape definitions (sphere, ellipse, and drop) were made for grape varieties. Cluster analysis revealed 4 sub-groups. The first sub-group included the 'Mevlana Sarisi' variety; the second sub-group included the 'Hönüsü', 'Hatun Parmagi', 'Dimiski', and 'Horoz Karasi' varieties; the third sub-group included the 'Ata Sarisi' variety; the fourth sub-group included the 'Baris', 'Helvani', 'Italia', and 'Red Globe' varieties. The variety in the first group had a geometrically ellipse-like shape, the largest length, and the smallest width. The size data were the smallest for the second sub-group. The third sub-group, with the ellipse-like shape, had the large size data. The grape varieties the closest to the sphere were classified in the fourth group, and these varieties had the large sizes.

Prognostic significance of malnutrition scores in elderly patients for the prediction of contrast-induced acute kidney injury.

BACKGROUND: Malnutrition reflects the general condition of a patient including physical condition, protein turnover, and immune competence. Contrast-induced acute kidney injury (CI-AKI) is a disorder that adversely affects the prognosis of older adults. In our study, we aimed to show the relationship between CI-AKI and malnutrition status in elderly patients over 65 years of age with chronic coronary artery disease (CAD). METHODS: Study enrolled 360 consecutive patients with coronary angiography performed because of chronic coronary artery disease. Patients pre-procedural and post-procedural blood samples were taken and prognostic nutritional index (PNI), controlling nutritional status (CONUT) score, and geriatric nutritional risk index (GNRI) malnutrition scores were calculated. RESULTS: The median age of the patients included in the study was 69 (67-72, IQR) and CI-AKI was seen in 91 (25.2%) patients. Univariate regression analysis showed that age, diabetes mellitus, baseline creatinine, body weight-adapted contrast agent, haemoglobin, left ventricular ejection fraction, CONUT score, PNI score, and GNRI score were independent predictors of CI-AKI. In model 1, increase in CONUT score (2 to 5) (OR: 3.21 (2.11-4.88), in model 2, increase in PNI score (37.4 to 45) (OR: 0.34, (0.24-0.49)), and in model 3, increase in GNRI score (89.5 to 103.8) (OR: 0.55, (0.38-0.81)) were independently associated with the presence of CI-AKI. PNI showed better results than other models in discriminating the predictable capability for CI-AKI. CONCLUSION: Malnutrition assessment of elderly patients before performing diagnostic or interventional coronary procedures could help clinicians to identify patients with elevated risk for CI-AKI.

The evaluation of the clinical utility of urocortin 1 and adrenomedullin versus proBNP in systolic heart failure.

OBJECTIVE: Urocortin 1 (UCN1) has vasodilator, diuretic, and natriuretic effects, and its expression increases in heart failure (HF). Adrenomedullin (ADM) increases cardiac output and lowers blood pressure in healthy men and in patients with heart failure. The aim of the study was to determine UCN1 and ADM levels in patients with HF, to evaluate the relationship of UCN1 and ADM with various clinical parameters, and to assess UCN1 and ADM as diagnostic markers in HF, in comparison with pro-brain natriuretic peptide (pro-BNP). METHODS: We investigated serum levels of UCN1, ADM, and pro BNP in 86 consecutive patients with systolic HF [ejection fraction (EF) ≤45%] and 85 healthy controls. Serum UCN1, ADM, and pro-BNP levels were measured with the ELISA method. Transthoracic echocardiography was performed to determine left ventricular EF and pulmonary artery systolic pressure. RESULTS: UCN1 and ADM levels were higher in HF patients (446.2±145.7 pg/mL, p<0.001; 87.9±4.2 pg/mL, p<0.001 respectively). UCN1 was positively correlated with pro-BNP (r=0.963, p<0.001), ADM (r=0.915, p<0.001), and NYHA (r=0.879, p<0.001); ADM was positively correlated with pro-BNP (r=0.956, p<0.001) and NYHA (r=0.944, p<0.001). Receiver operating characteristic curves yielded an area under the curve of 1.00 (p<0.001) for UCN1, 1.00 (p<0.001) for ADM, and 0.99 (p<0.001) for pro-BNP in the diagnosis of HF. CONCLUSION: UCN1 and ADM increase with worsening HF and left ventricular dysfunction. They may be used as diagnostic biomarkers in systolic HF, but the incremental value of measuring UCN1 and ADM in patients tested for pro-BNP is questionable.

The Relationship between P & QT Dispersions and Presence & Severity of Stable Coronary Artery Disease.

BACKGROUND AND OBJECTIVES: The study aimed to evaluate the correlation between electrocardiographic (ECG) parameters and presence and extent of coronary artery disease (CAD) to indicate the usefulness of these parameters as predictors of severity in patients with stable CAD. SUBJECTS AND METHODS: Two hundred fifty patients, without a history of any cardiovascular event were included in the study. The ECG parameters were measured manually by a cardiologist before coronary angiography. The patients were allocated into five groups: those with normal coronary arteries (Group 1), non-critical coronary lesions (Group 2), one, two and three vessel disease (Group 3, Group 4 and Group 5, respectively. RESULTS: Group 1 had the lowest P wave dispersion (PWD) and P wave (Pmax), QT interval (QTmax), QT dispersion (QTd), corrected QT dispersion (QTcd) and QT dispersion ratio (QTdR), while the patients in group 5 had the highest values of these parameters. Gensini score and QTmax, QTd, QTcmax, QTcd, QTdR, Pmax, and PWD were positively correlated. QTdR was the best ECG parameter to differentiate group 1 and 2 from groups with significant stenosis (groups 3, 4, and 5) (area under curve [AUC] 0.846). QTdR was the best ECG parameter to detect coronary arterial narrowing lesser than 50% and greater than 50%, respectively (AUC 0.858). CONCLUSION: Presence and severity of CAD can be determined by using ECG in patients with stable CAD and normal left ventricular function.