Study on adsorption behavior and separation efficiency of naturally occurring clay for some elements by batch experiments

This paper describes the versatile nature of clay that was obtained from Suez Gulf, Suez city, Egypt, as a new low cost natural resource which is non toxic to ecosystem and highly effective adsorbent material. The properties of the natural clay and its significance in removing Th(IV) and Ce(IV) as a representative of tetravalent actinides and lanthanides, respectively, La(III) as a representative of trivalent lanthanides as well as homologues of americium and Sr(II) as one of the fission products, in aqueous solutions have been studied, in order to consider its application for nuclear waste treatment. Batch experiments have been carried out to determine the effect of various factors such as initial metal ion concentration, clay dose, pH, contact time, and temperature on the adsorption process. The results have dictated that, the adsorption efficiency of the natural clay was significantly high at pH = 4. Maximum metal ion uptake capacity of clay has been obtained from batch studies was 99.24, 98.21, 77.76 and 57.94% for Th(IV), Ce(IV), La(III) and Sr(II), respectively. The thermodynamic parameters (ΔHo, ΔSo and ΔGo) have been calculated from the temperature dependent adsorption isotherms. Furthermore, separation factors (α) have been calculated in order to separate these elements from each other at pH = 4

Formal Analysis and Estimation of Chance in Datasets Based on Their Properties

Machine learning research, particularly in genomics, is often based on wide shaped datasets, i.e. datasets having a large number of features, but a small number of samples. Such configurations raise the possibility of chance influence (the increase of measured accuracy due to chance correlations) on the learning process and the evaluation results. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, we investigate the influence of chance on prediction and show its significant effects on wide shaped datasets. First, we empirically demonstrate how significant the influence of chance in such datasets is by showing that prediction models trained on thousands of randomly generated datasets can achieve high accuracy. This is the case even when using cross-validation. We then provide a formal analysis of chance influence and design formal chance influence estimators based on the dataset parameters, namely its sample size, the number of features, the number of classes and the class distribution. Finally, we provide an in-depth discussion of the formal analysis including applications of the findings and recommendations on chance influence mitigation

On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking

Average Hausdorff distance is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, it is used to compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average Hausdorff distance making it less suitable for applications in segmentation performance assessment. To mitigate this error, we present a modified calculation of this performance measure that we have coined "balanced average Hausdorff distance". To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using both performance measures. We calculated the Kendall rank correlation coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by balanced average Hausdorff distance had a significantly higher median correlation (1.00) than those by average Hausdorff distance (0.89). In 200 total rankings, the former misranked 52 whilst the latter misranked 179 segmentations. Balanced average Hausdorff distance is more suitable for rankings and quality assessment of segmentations than average Hausdorff distance

An evaluation of performance measures for arterial brain vessel segmentation

Background: Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation. Methods: To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient. Results: The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation. Conclusions: Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches

Relationship between obesity, physical activity, sleeping hours and red blood cell parameters in adult Sudanese population: Effect of exercise and sleep hours on RBC parameters

Ideal body weight with proper physical activity and good sleep are essential parameters for good quality of life. This study is concerned with assessing the association of general obesity, physical activity and sleeping hours with hemoglobin (Hb) concentration and red blood cell (RBC) parameters in healthy adults in Sudan. In this cross sectional study, 1086 healthy adults between 20 and 60 years were included out of which 275 were males and 811 were females. A complete blood count (CBC) was performed for Hb, RBC count, PCV, MCH and MCHC using Sysmex KX-21 automated hematology analyzer. The median and 95 percentile (2.5th to 97.5th) range values for Hb and RBC count in underweight were 13.0 (Range: 9.6-16.7) g/dl and 4.6 (Range: 3.6-5.8) ×103/µL respectively, while Hb and RBC count in obese were 13.1 (Range: 10.4-17.0) g/dl and 4.6 (Range: 3.7-5.9) ×103/µL respectively, with no significant difference. The RBC count (p=0.004) and Hb (p?0.001) were significantly high in physically active compared to physically inactive participants; whereas the hemoglobin concentration (p=0.047), red blood cells (p=0.007) and hematocrit (p?0.001) values were significantly low in long-term sleep compared to normal sleeping hours. In conclusion, there were no significant differences in hemoglobin concentration, RBC count, PCV, MCH and MCHC between unde weight, normal weight, overweight and obese persons. Increased physical activity was associated with higher Hb levels and RBC counts, while long-term sleep showed lower Hb and RBCs

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method-the U-net -is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of similar to 0.88, a 95HD of similar to 47 voxels and an AVD of similar to 0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice similar to 0.76, 95HD similar to 59, AVD similar to 1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Paitents with Cerebrovascular Disease

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method—the U-net—is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologie

Efficacy of Mesenchymal Stem Cells in Suppression of Hepatocarcinorigenesis in Rats: Possible Role of Wnt Signaling

<p>Abstract</p> <p>Background</p> <p>The present study was conducted to evaluate the tumor suppressive effects of bone marrow derived mesenchymal stem cells (MSCs) in an experimental hepatocellular carcinoma (HCC) model in rats and to investigate the possible role of Wnt signaling in hepato-carcinogenesis.</p> <p>Methods</p> <p>Ninety rats were included in the study and were divided equally into: Control group, rats which received MSCs only, rats which received MSCs vehicle only, HCC group induced by diethylnitroseamine (DENA) and CCl_<b>4</b>, rats which received MSCs after HCC induction, rats which received MSCs before HCC induction. Histopathological examination and gene expression of Wnt signaling target genes by real time, reverse transcription-polymerase chain reaction (RT-PCR) in rat liver tissue, in addition to serum levels of ALT, AST and alpha fetoprotein were performed in all groups.</p> <p>Results</p> <p>Histopathological examination of liver tissue from animals which received DENA-CCl₄only, revealed the presence of anaplastic carcinoma cells and macro-regenerative nodules type II with foci of large and small cell dysplasia. Administration of MSCs into rats after induction of experimental HCC improved the histopathological picture which showed minimal liver cell damage, reversible changes, areas of cell drop out filled with stem cells. Gene expression in rat liver tissue demonstrated that MSCs downregulated <it>β-catenin</it>, proliferating cell nuclear antigen (<it>PCNA</it>), <it>cyclin D </it>and <it>survivin </it>genes expression in liver tissues after HCC induction. Amelioration of the liver status after administration of MSCs has been inferred by the significant decrease of ALT, AST and Alpha fetoprotein serum levels. Administration of MSCs before HCC induction did not show any tumor suppressive or protective effect.</p> <p>Conclusions</p> <p>Administration of MSCs in chemically induced HCC has tumor suppressive effects as evidenced by down regulation of Wnt signaling target genes concerned with antiapoptosis, mitogenesis, cell proliferation and cell cycle regulation, with subsequent amelioration of liver histopathological picture and liver function.</p

Comparative study of the chemical composition and anti-proliferative activities of the aerial parts and roots of Apium graveolens L. (celery) and their biogenic nanoparticles

Apiaceae plants are multipurpose folk remedies and bioactive foods that show a remarkable ability to biosynthesize a large number of secondary metabolites with antitumor and chemopreventive potential. Among the various members of the Apiaceae, celery (Apium graveolens L.) has long been used as a popular edible and medicinal plant owing to its plentiful health benefits and nutraceutical properties; however, the anticancer potential of this important species has been seldom studied, mostly focusing on its seeds. Therefore, this work was designed to delve into the chemical composition and anti-proliferative potential of the total ethanolic extracts of the aerial parts (TEEAGA) and roots (TEEAGR) of A. graveolens var. dulce (Mill.) Pers. as well as their green synthesized silver nanoparticles (AgNPs). In general, both TEEAGA and TEEAGR exhibited moderate to potent inhibitory activities against human liver (HepG-2), colon (Caco-2), and breast (MCF-7) cancer cell lines, with interesting IC50 profiles [(41.37 ± 0.12, 27.65 ± 0.27, and 9.48 ± 0.04 μg/mL) and (11.58 ± 0.02, 7.13 ± 0.03, and 6.58 ± 0.02 μg/mL), respectively] as compared with doxorubicin, while more pronounced anti-proliferative effects were observed for their biogenic AgNPs, which showed IC50 values ranging between 25.41 ± 0.16 and 1.37 ± 0.03 μg/mL. Moreover, HPLC‒HESI‒HRMS-based metabolomics analysis of both extracts showed the presence of a varied group of secondary metabolites, including flavonoids, phenylpropanoids, phthalides, coumarins, and sesquiterpenes that further displayed moderate to promising binding affinities to the active site of cyclin G-associated kinase (GAK), particularly graveobioside A, graveobioside B, and celeroside C, suggesting their possible contribution as GAK modulators to the anti-proliferative potential of celery. These findings can help broaden future research on the utilization of different parts of celery and their NPs as functional foods and medicines in cancer chemoprevention and therapy