A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

Evaluation of the antioxidant activity of non-transformed and transformed pineapple: a comparative study.

Pineapple has several beneficial properties including antioxidant activity. We investigated the antioxidant effect of different extracts of non-transformed (S) and transformed pineapple (with the magainin gene construct, [TS], for disease resistance). They were examined using 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging, oxygen radical absorbance capacity (ORAC) and lipid peroxidation assays besides phenolic and flavonoid contents. HPLC analysis was carried out to identify the possible components responsible for the differences observed. The present study indicates that the ORAC values of extracts range from 9.5 to 26.4, similar to or higher than those for some fruits and vegetables. The HPLC analysis shows that the main compounds present are ascorbic acid, quercetin, flavone-3-ols, flavones, cinnamic acids. The TS core Et. extract exhibited slightly higher concentration of ascorbic acid and considerably higher concentration of flavon-3-ols. Our study, in general, indicates that the transformation event has caused only marginal difference in antioxidant activity. Moreover the TS samples showed more antioxidant activity in some aspects and also exhibit more flavonoid content. It appears that plant cell transformation has only caused minor and favourable changes in the overall chemical composition. Thus the TS pineapple variety may have potential applications in human health like its non-transformed counterpart.