Scopus İndeksli Yayınlar Koleksiyonu

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    Predictive Factors Affecting the Success of Nephrectomy for the Treatment of Nephrogenic Hypertension: Multicenter Study
    (2021) Vuruskan, Ediz; Ercil, Hakan; Unal, Umut; Alma, Ergun; Anil, Hakan; Sumbul, Hilmi Erdem; Deniz, Mehmet Eflatun; Goren, Mehmet Resit; 33873196
    Introduction: The aim of our study is to evaluate the predictive factors affecting the success of treatment with nephrectomy in patients with poorly functioning kidney and nephrogenic hypertension. Methods: Data for patients who underwent nephrectomy with a diagnosis of nephrogenic hypertension in 3 centers between May 2010 and January 2020 were analyzed. In the postoperative period, if the blood pressure (BP) was below 140/90 mm Hg without medical treatment, it was accepted as complete response; if the arterial BP was below 140/90 mm Hg with medical treatment or less medication, it was accepted as partial response; and if BP did not decrease to normal values, it was accepted as unresponsive. Demographic characteristics, duration of hypertension, preoperative and postoperative BP values, and presence of metabolic syndrome were statistically evaluated. Results: Our study consisted of 91 patients with a mean preoperative hypertension duration of 23.3 +/- 12.1 months. Among patients, 42 (46.2%) had complete response, 18 (19.8%) had partial response, and 31 (34.0%) had no response. Preoperative systolic and diastolic BP values were not effective on treatment success (p = 0.071, p = 0.973, respectively), but the increase in age and hypertension duration (p = 0.030 and p < 0.001, respectively) and the presence of metabolic syndrome (p = 0.002) significantly decreased the complete response rates. Conclusions: Preoperative hypertension duration, advanced age, and presence of metabolic syndrome are predictive factors affecting the response to treatment in patients who undergo nephrectomy due to nephrogenic hypertension.
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    Blood pressure prediction from speech recordings
    (2020) Ankishan, Haydar
    The aim of this study is to extract new features to show the relationship between speech recordings and blood pressure (BP). For this purpose, a database consisting of / a / vowels with different BP values under the same room and environment conditions is presented to the literature. Convolutional Neural Networks- Regression (CNN-R), Support Vector Machines- Regression (SVMs-R) and Multi Linear Regression (MLR) are used in this study to predict BP with extracted features. From the experiments, the highest accuracy rates of BP prediction from / a / vowel have been obtained based on Systolic BP values with CNNR. In the study, 89.43 % for MLR, 92.15 % for SVM-R and 93.65 % for CNN-R are obtained when ReliefF has been used. When the root mean square errors (RMSE) are considered, the lowest error value is obtained with CNN-R as RMSE = 0.2355. In conclusion, it can be observed that the proposed feature vector (FVx) shows a relationship between BP and the human voices, and in this direction, it can be used as an FVx in a system that will be developed in order to follow the tension of individuals. (C) 2020 Elsevier Ltd. All rights reserved.