Predicting Physico-Chemical Characteristics of Brain Tumour

This research proposes a series of algorithms that work for improved Brain Tumor identification and classification. The Brain Tumor study based on the MRI image will effectively resolve the classification method for diagnosis of brain tumors. There are three stages: Extraction of features, Reduction of features and classification. Extraction function and reduction of functionality used for two algorithms. The extracted characteristics are Mean, Standard deviation, Curtosis, Skewness, Entropy Contrast, Variance, Smoothness, Correlation and Power. The result is then supplied to Support Vector Machine (SVM) for the Benign or Malignant classification of tumours.

Clinicopathological Implications of Proteinuria after Long-Term Isolated Hematuria due to Thin Basement Membrane Nephropathy and Focal Segmental Glomerulosclerosis

A 45-year-old obese man presented with persistent hematuria for 21 years. At the age of 37, he developed hypertension and proteinuria which later increased up to 1.6 g/g creatinine. Kidney biopsy revealed thin basement membrane nephropathy (TBMN) and focal segmental glomerulosclerosis (FSGS), which explained his urinary abnormalities. Although a subgroup of TBMN can be complicated by FSGS, his FSGS was associated with obesity because of its histological features. Reduction of body weight and increasing a dose of angiotensin-receptor blocker could transiently reduce the amount of proteinuria. Clinicopathological implications of proteinuria after long-term hematuria by TBMN and FSGS were further discussed.