Exploring the potential of Red Kidney Bean (Phaseolus vulgaris) to develop protein Based Product for food Applications.

ABSTRACT Protein isolate was prepared from red kidney beans and its functional properties were evaluated at different pH levels to access its suitability for food applications. Carbohydrates, crude protein, crude fiber, crude fat and ash contents of red kidney bean seeds were found to be 53.02±1.14%, 25.78±0.77%, 6.82±0.31%, 1.92±0.15% and 4.34±0.20%, respectively. Magnesium, calcium, sodium, potassium and iron were observed as macro elements in red kidney bean seeds. Protein solubility, emulsification, gelation as well as foaming properties of the bean protein isolate were significantly (P≤0.05) affected by different pH levels. The solubility, emulsifying activity and stability as well as foam capacity of the protein isolate were dependant with minimal values observed at pH 4 while maximum at pH 10. Contrarily, the stability of foam was highest at pH 4 while a decreasing trend in foam stability was observed with increase of pH. Gelation properties improved at acidic pH with maximum gelation capacity observed at pH 4 while these properties decreased at alkaline conditions. Conclusively, red kidney beans can be utilized to prepare protein isolate whose functional properties can be modified by changing the pH of the environment for better utilization in the food formulation systems

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases. Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high- and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95-percentile Hausdorff distance (95HD); and via visual qualitative rating. Results: The BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The BRAVE-NET model was also the most resistant toward false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration Hilbert et al. Fully-Automated Arterial Brain Vessel Segmentation of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component. Discussion: We present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease and an external set of healthy volunteers. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases

Amelioration effect of 18β-Glycyrrhetinic acid on methylation inhibitors in hepatocarcinogenesis -induced by diethylnitrosamine

Aimsuppression of methylation inhibitors (epigenetic genes) in hepatocarcinogenesis induced by diethylnitrosamine using glycyrrhetinic acid.MethodIn the current work, we investigated the effect of sole GA combined with different agents such as doxorubicin (DOX) or probiotic bacteria (Lactobacillus rhamanosus) against hepatocarcinogenesis induced by diethylnitrosamine to improve efficiency. The genomic DNA was isolated from rats’ liver tissues to evaluate either methylation-sensitive or methylation-dependent resection enzymes. The methylation activity of the targeting genes DLC-1, TET-1, NF-kB, and STAT-3 was examined using specific primers and cleaved DNA products. Furthermore, flow cytometry was used to determine the protein expression profiles of DLC-1 and TET-1 in treated rats’ liver tissue.ResultsOur results demonstrated the activity of GA to reduce the methylation activity in TET-1 and DLC-1 by 33.6% and 78%, respectively. As compared with the positive control. Furthermore, the association of GA with DOX avoided the methylation activity by 88% and 91% for TET-1 and DLC-1, respectively, as compared with the positive control. Similarly, the combined use of GA with probiotics suppressed the methylation activity in the TET-1 and DLC-1 genes by 75% and 81% for TET-1 and DLC-1, respectively. Also, GA and its combination with bacteria attenuated the adverse effect in hepatocarcinogenesis rats by altering potential methylomic genes such as NF-kb and STAT3 genes by 76% and 83%, respectively.ConclusionGA has an ameliorative effect against methylation inhibitors in hepatocellular carcinoma (HCC) by decreasing the methylation activity genes

In vitro cytotoxicity of Withania somnifera (L.) roots and fruits on oral squamous cell carcinoma cell lines: a study supported by flow cytometry, spectral, and computational investigations

Oral cancer is a severe health problem that accounts for an alarmingly high number of fatalities worldwide. Withania somnifera (L.) Dunal has been extensively studied against various tumor cell lines from different body organs, rarely from the oral cavity. We thus investigated the cytotoxicity of W. somnifera fruits (W-F) and roots (W-R) hydromethanolic extracts and their chromatographic fractions against oral squamous cell carcinoma (OSCC) cell lines [Ca9-22 (derived from gingiva), HSC-2, HSC-3, and HSC-4 (derived from tongue)] and three normal oral mesenchymal cells [human gingival fibroblast (HGF), human periodontal ligament fibroblast (HPLF), and human pulp cells (HPC)] in comparison to standard drugs. The root polar ethyl acetate (W-R EtOAc) and butanol (W-R BuOH) fractions exhibited the strongest cytotoxicity against the Ca9-22 cell line (CC50 = 51.8 and 40.1 μg/mL, respectively), which is relatively the same effect as 5-FU at CC50 = 69.4 μM and melphalan at CC50 = 36.3 μM on the same cancer cell line. Flow cytometric analysis revealed changes in morphology as well as in the cell cycle profile of the W-R EtOAc and W-R BuOH-treated oral cancer Ca9-22 cells compared to the untreated control. The W-R EtOAc (125 μg/mL) exerted morphological changes and induced subG1 accumulation, suggesting apoptotic cell death. A UHPLC MS/MS analysis of the extract enabled the identification of 26 compounds, mainly alkaloids, withanolides, withanosides, and flavonoids. Pharmacophore-based inverse virtual screening proposed that BRD3 and CDK2 are the cancer-relevant targets for the annotated withanolides D (18) and O (12), and the flavonoid kaempferol (11). Molecular modeling studies highlighted the BRD3 and CDK2 as the most probable oncogenic targets of anticancer activity of these molecules. These findings highlight W. somnifera’s potential as an affordable source of therapeutic agents for a range of oral malignancies

Complex and unexpected dynamics in simple genetic regulatory networks

Peer reviewedPublisher PD