Investigating purification and activity analysis of urease enzyme extracted from jack bean source: A green chemistry approach.

Urease is an enzyme of historical importance in the field of biochemistry, generally microbial and plant urease is the primary sources of urease. The significant applications of urease enzyme are found to be foremost in food industry, medical equipment's and biosensors. In this work, urease has been extracted from Jack bean meal using ammonium sulphate and acetone precipitation. A significant amount of urease was precipitated and concentrated at 60% saturated solution of ammonium sulphate. The obtained precipitates were dissolved in 50 mM phosphate buffer (pH 8) after centrifugation, and subjected to sodium dodecyl-sulphate polyacrylamide gel electrophoresis (SDS-PAGE) to determine the molecular weight of urease. Results obtained from the SDS-PAGE were validated using Zymography. Anion exchange chromatography was used to separate the desired protein at different pH (7.0, 7.5 and 8.0). The eluted fractions were assessed for urease activity using phenol-nitroprusside dependent ammonia release assay. Under these assay conditions, one unit of urease activity was calibrated as the amount of enzyme liberating 1 µM of NH3 from urea per unit time. The eluted fraction and Zymography analysis show the purified urease observed at 90 kDa and activity of purified urease, respectively. The obtained results for specific activity (173.67Units mg) and % purification (99.71%) for urease has been compared with the available literature, which are found to be in close relation with existing results. The proposed method is a novel approach which has recorded highest % purification and specific activity. Furthermore, it can be suitable for extracting urease from jack bean source for various industrial applications.

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection.

Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, "BIRADS C and BIRADS D." Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.