Comparing online and face-to-face student counselling: what therapeutic goals are identified and what are the implications for educational providers?

Online counselling is increasingly being used as an alternative to face-to-face student counselling. Using an exploratory mixed methods design, this project investigates the practice by examining the types of therapeutic goals that 11 to 25 year olds identify online in routine practice. These goals are then compared to goals identified in equivalent school and community-based counselling services. 1,137 online goals (expressed by 504 young people) and 221 face-to-face goals (expressed by 220 young people) were analysed for key themes using grounded theory techniques. This analysis identified three core categories (1) Intrapersonal Goals, (2) Interpersonal Goals, and (3) Intrapersonal Goals directly related to others. Further statistical analysis of these themes indicated that online and face-to-face services appear to be being used in different ways by students. These differences are discussed alongside the implications for professionals working in educational settings

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images

Cataloged from PDF version of article.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms

Identification of Novel Reference Genes Based on MeSH Categories

Cataloged from PDF version of article.Transcriptome experiments are performed to assess protein abundance through mRNA expression analysis. Expression levels of genes vary depending on the experimental conditions and the cell response. Transcriptome data must be diverse and yet comparable in reference to stably expressed genes, even if they are generated from different experiments on the same biological context from various laboratories. In this study, expression patterns of 9090 microarray samples grouped into 381 NCBI-GEO datasets were investigated to identify novel candidate reference genes using randomizations and Receiver Operating Characteristic (ROC) curves. The analysis demonstrated that cell type specific reference gene sets display less variability than a united set for all tissues. Therefore, constitutively and stably expressed, origin specific novel reference gene sets were identified based on their coefficient of variation and percentage of occurrence in all GEO datasets, which were classified using Medical Subject Headings (MeSH). A large number of MeSH grouped reference gene lists are presented as novel tissue specific reference gene lists. The most commonly observed 17 genes in these sets were compared for their expression in 8 hepatocellular, 5 breast and 3 colon carcinoma cells by RT-qPCR to verify tissue specificity. Indeed, commonly used housekeeping genes GAPDH, Actin and EEF2 had tissue specific variations, whereas several ribosomal genes were among the most stably expressed genes in vitro. Our results confirm that two or more reference genes should be used in combination for differential expression analysis of large-scale data obtained from microarray or next generation sequencing studies. Therefore context dependent reference gene sets, as presented in this study, are required for normalization of expression data from diverse technological backgrounds. © 2014 Ersahin et al

Microscopic image classification using sparsity in a transform domain and Bayesian learning

Some biomedical images show a large quantity of different junctions and sharp corners. It is possible to classify several types of biomedical images in a region covariance approach. Cancer cell line images are divided into small blocks and covariance matrices of image blocks are computed. Eigen-values of the covariance matrices are used as classification parameters in a Bayesian framework using the sparsity of the parameters in a transform domain. The efficiency of the proposed method over classification using standard Support Vector Machines (SVM) is demonstrated on biomedical image data. © 2011 EURASIP

Inhibition of Akt signaling in hepatoma cells induces apoptotic cell death independent of Akt activation status

Cataloged from PDF version of article.The serine/threonine kinase Akt, a downstream effector of phosphatidylinositol 3-kinase (PI3K), is involved in cell survival and anti-apoptotic signaling. Akt has been shown to be constitutively expressed in a variety of human tumors including hepatocellular carcinoma (HCC). In this report we analyzed the status of Akt pathway in three HCC cell lines, and tested cytotoxic effects of Akt pathway inhibitors LY294002, Wortmannin and Inhibitor VIII. In Mahlavu human hepatoma cells Akt was constitutively activated, as demonstrated by its Ser473 phosphorylation, downstream hyperphosphorylation of BAD on Ser136, and by a specific cell-free kinase assay. In contrast, Huh7 and HepG2 did not show hyperactivation when tested by the same criteria. Akt enzyme hyperactivation in Mahlavu was associated with a loss of PTEN protein expression. Akt signaling was inhibited by the upstream kinase inhibitors, LY294002, Wortmannin, as well as by the specific Akt Inhibitor VIII in all three hepatoma cell lines. Cytotoxicity assays with Akt inhibitors in the same cell lines indicated that they were all sensitive, but with different IC50 values as assayed by RT-CES. We also demonstrated that the cytotoxic effect was through apoptotic cell death. Our findings provide evidence for its constitutive activation in one HCC cell line, and that HCC cell lines, independent of their Akt activation status respond to Akt inhibitors by apoptotic cell death. Thus, Akt inhibition may be considered as an attractive therapeutic intervention in liver cancer. © Springer Science+Business Media, LLC 2010

Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance

In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. WT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L 2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method. © 2012 IEEE

Iterative H-minima-based marker-controlled watershed for cell nucleus segmentation

Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry

Liver cancer cells are sensitive to Lanatoside C induced cell death independent of their PTEN status

Background Hepatocellular carcinoma is the second deadliest cancer with limited treatment options. Loss of PTEN causes the P13K/Akt pathway to be hyperactive which contributes to cell survival and resistance to therapeutics in various cancers, including the liver cancer. Hence molecules targeting this pathway present good therapeutic strategies for liver cancer. Hypothesis It was previously reported that Cardiac glycosides possessed antitumor activity by inducing apoptosis of multiple cancer cells through oxidative stress. However, whether Cardiac glycoside Lanatoside C can induce oxidative stress in liver cancer cells and induce cell death both in vitro and in vivo remains unknown. Methods Cell viability was measured by SRB assay. Cell death analysis was investigated by propidium iodide staining with flow cytometry and PARP cleavage. DCFH-DA staining and cytometry were used for intracellular ROS measurement. Protein levels were analyzed by western blot analysis. Antitumor activity was investigated on mice xenografts in vivo. Results In this study, we found that Cardiac glycosides, particularly Lanatoside C from Digitalis ferruginea could significantly inhibit PTEN protein adequate Huh7 and PTEN deficient Mahlavu human liver cancer cell proliferation by the induction of apoptosis and G2/M arrest in the cells. Lanatoside C was further shown to induce oxidative stress and alter ERK and Akt pathways. Consequently, JNK1 activation resulted in extrinsic apoptotic pathway stimulation in both cells while JNK2 activation involved in the inhibition of cell survival only in PTEN deficient cells. Furthermore, nude mice xenografts followed by MRI showed that Lanatoside C caused a significant decrease in the tumor size. In this study apoptosis induction by Lanatoside C was characterized through ROS altered ERK and Akt pathways in both PTEN adequate epithelial and deficient mesenchymal liver cancer cells. Conclusion The results indicated that Lanatoside C could be contemplated in liver cancer therapeutics, particularly in PTEN deficient tumors. This is due to Lanatoside C's stress inducing action on ERK and Akt pathways through differential activation of JNK1 and JNK2 by GSK3β. © 2015 Elsevier GmbH. © 2016 Elsevier GmbH. All rights reserved

A multiplication-free framework for signal processing and applications in biomedical image analysis

A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix. © 2013 IEEE

Quantification of fractional flow reserve based on angiographic image data

Coronary angiography provides excellent visualization of coronary arteries, but has limitations in assessing the clinical significance of a coronary stenosis. Fractional flow reserve (FFR) has been shown to be reliable in discerning stenoses responsible for inducible ischemia. The purpose of this study is to validate a technique for FFR quantification using angiographic image data. The study was carried out on 10 anesthetized, closed-chest swine using angioplasty balloon catheters to produce partial occlusion. Angiography based FFR was calculated from an angiographically measured ratio of coronary blood flow to arterial lumen volume. Pressure-based FFR was measured from a ratio of distal coronary pressure to aortic pressure. Pressure-wire measurements of FFR (FFRP) correlated linearly with angiographic volume-derived measurements of FFR (FFRV) according to the equation: FFRP = 0.41 FFRV + 0.52 (P-value < 0.001). The correlation coefficient and standard error of estimate were 0.85 and 0.07, respectively. This is the first study to provide an angiographic method to quantify FFR in swine. Angiographic FFR can potentially provide an assessment of the physiological severity of a coronary stenosis during routine diagnostic cardiac catheterization without a need to cross a stenosis with a pressure-wire