A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network.

Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks to these images, some methods have so far been proposed in order to distinguish between benign and malignant brain tumors. Many systems attempting to define these tumors are based on tissue analysis methods. However, various factors such as the quality of an MRI device, noisy images and low image resolution may decrease the quality of MRI images. To eliminate these problems, super resolution approaches are preferred as a complementary source for brain tumor images. The proposed method benefits from single image super resolution (SISR) and maximum fuzzy entropy segmentation (MFES) for brain tumor segmentation on an MRI image. Later, pre-trained ResNet architecture, which is a convolutional neural network (CNN) architecture, and support vector machine (SVM) are used to perform feature extraction and classification, respectively. It was observed in experimental studies that SISR displayed a higher performance in terms of brain tumor segmentation. Similarly, it displayed a higher performance in terms of classifying brain tumor regions as well as benign and malignant brain tumors. As a result, the present study indicated that SISR yielded an accuracy rate of 95% in the diagnosis of segmented brain tumors, which exceeds brain tumor segmentation using MFES without SISR by 7.5%.

Is there a relationship between serum vaspin levels and insulin resistance in chronic renal failure?

OBJECTIVE: Chronic kidney disease (CKD) patients have insulin secretion disorders and resistance to insulin effects, that is responsible for the development of cardiovascular events. Vaspin is an adipocytokine that regulates glucose and lipid metabolism. We aimed to determine the serum vaspin levels and its relationship with insulin resistance in CKD patients. METHODS: In the study groups, serum vaspin levels, anthropometric parameters and routine blood tests were measured. The serum vaspin levels were examined by the enzyme-linked immunosorbent assay (ELISA) and insulin resistance was determined by the homeostasis model assessment of insulin resistance (HOMA-IR) formula. RESULTS: The serum vaspin, HOMA-IR index and insulin levels were observed significantly high in the CKD group in comparison with the control group. No correlation was found between the serum vaspin level and the anthropometric and metabolic values. The serum vaspin level was positively correlated with the fasting plasma glucose and age but without statistical significance. CONCLUSION: Insulin resistance and hyperinsulinemia contribute to the development of cardiovascular complications in CKD. We consider that the increase in the serum vaspin level is a consequence of the reduced renal excretion in the CKD and increases in response to insulin resistance.

An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine.

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

Oxidative stress and antioxidant parameters in neutropenic patients secondary to chemotherapy.

OBJECTIVE: Neutropenia is a serious adverse event that necessitates dosage reduction in patients receiving chemotherapy. In this study, we evaluated the oxidative stress and antioxidant parameters in neutropenic patients after chemotherapy both during the neutropenic period and after successful treatment of neutropenia with filgrastim. METHODS: We studied paraoxonase (PON1), arylesterase (ARE), malondialdehyde (MDA), high-density lipoprotein (HDL), lactate dehydrogenase (LDH), and alkaline phosphatase (ALP) in addition to routine biochemical and hematologic parameters. SPSS 12.0 was used for statistical evaluation of data (SPSS, Chicago, IL, USA). RESULTS: In our study, PON1, HDL, and LDH levels during the period of active neutropenia were statistically significantly higher than these levels were after resolution of neutropenia (P<0.05); MDA and ALP levels were statistically significantly lower during the period of active neutropenia (P<0.05). CONCLUSIONS: Overall, free oxygen radicals (FOR) were increased and antioxidant parameters were decreased with resolution of neutropenia. This is probably due to FOR produced by the increased number of neutrophils rather than tumor burden.

Serum apelin levels in patients with thyroid dysfunction.

Adipocytes are not only for energy storage, but are also functionally active cells, producing biologically active peptides called adipocytokines. Adipocytokines control nutrition, thermogenesis, immunity, thyroid and reproductive hormones, and neuroendocrine functions. One of the most important new members of this family is apelin. In patients with thyroid dysfunctions, there are usually changes in weight, thermogenesis and adipose tissue lipolysis. Here, we investigated the serum apelin levels in different thyroid hormone states. Our study group consisted of the following patients: 32 thyrotoxicosis, 32 subclinical hyperthyroidism, 31 hypothyroidism, 34 subclinical hypothyroidism and 31 healthy control cases. In addition to routine blood tests, serum free T3 (FT3), free T4 (FT4), TSH and apelin levels were measured, and the body mass index (BMI) was recorded. In terms of the demographic characteristics, age and BMI, there was no statistically significant difference between the groups (P>0.05). The mean serum apelin levels of the groups were as follows: thyrotoxicosis group, 4.6±1.9 ng/ml; subclinical hyperthyroidism group, 3.7±1.9 ng/ml; hypothyroid group, 4.8±2.5 ng/ml; subclinical hypothyroidism group, 4.3±2.2 ng/mL; and control group, 3.4±1.4 ng/ml, respectively. There was no statistically significant difference in terms of the mean apelin levels between the groups (P>0.05). The hypothyroid group had the highest and the control group had the lowest mean apelin levels. As a result, the apelin levels were higher in both the patients with hypothyroidism and hyperthyroidism, in comparison with the normal population, but without statistical significance.