Nursing Students’ Perception of the Stigma of Mental Illness

Background: Mental health disorders are highly prevalent in the U.S. Nursing students’ perceptions regarding the stigma of mental illness will impact the quality of care delivered and the patients’ outcomes. Method: Data was collected from 64 sophomore students. Five open ended questions were distributed to the students during the first class. All the surveys were collected by a volunteer student and were placed in the instructor’s mailbox in a sealed envelope. Results: The results revealed three categories: students ‘perceptions of the causes of mental illness stigmatization, their own perception of mental illness, and their perception on how to break the cycle of stigmatization of mental illness. Conclusion: Nursing students provided insightful perceptions regarding the causes of the stigma and possible interventions. Collaborative efforts to break the stigma of mental illness include: education, acceptance, increasing awareness, and better portrayal in the media

Epigenetic regulation of autophagy: A key modification in cancer cells and cancer stem cells.

Aberrant epigenetic alterations play a decisive role in cancer initiation and propagation via the regulation of key tumor suppressor genes and oncogenes or by modulation of essential signaling pathways. Autophagy is a highly regulated mechanism required for the recycling and degradation of surplus and damaged cytoplasmic constituents in a lysosome dependent manner. In cancer, autophagy has a divergent role. For instance, autophagy elicits tumor promoting functions by facilitating metabolic adaption and plasticity in cancer stem cells (CSCs) and cancer cells. Moreover, autophagy exerts pro-survival mechanisms to these cancerous cells by influencing survival, dormancy, immunosurveillance, invasion, metastasis, and resistance to anti-cancer therapies. In addition, recent studies have demonstrated that various tumor suppressor genes and oncogenes involved in autophagy, are tightly regulated via different epigenetic modifications, such as DNA methylation, histone modifications and non-coding RNAs. The impact of epigenetic regulation of autophagy in cancer cells and CSCs is not well-understood. Therefore, uncovering the complex mechanism of epigenetic regulation of autophagy provides an opportunity to improve and discover novel cancer therapeutics. Subsequently, this would aid in improving clinical outcome for cancer patients. In this review, we provide a comprehensive overview of the existing knowledge available on epigenetic regulation of autophagy and its importance in the maintenance and homeostasis of CSCs and cancer cells

Dual-Branch MRC Receivers under Spatial Interference Correlation and Nakagami Fading

Despite being ubiquitous in practice, the performance of maximal-ratio combining (MRC) in the presence of interference is not well understood. Because the interference received at each antenna originates from the same set of interferers, but partially de-correlates over the fading channel, it possesses a complex correlation structure. This work develops a realistic analytic model that accurately accounts for the interference correlation using stochastic geometry. Modeling interference by a Poisson shot noise process with independent Nakagami fading, we derive the link success probability for dual-branch interference-aware MRC. Using this result, we show that the common assumption that all receive antennas experience equal interference power underestimates the true performance, although this gap rapidly decays with increasing the Nakagami parameter $m_{\text{I}}$ of the interfering links. In contrast, ignoring interference correlation leads to a highly optimistic performance estimate for MRC, especially for large $m_{\text{I}}$. In the low outage probability regime, our success probability expression can be considerably simplified. Observations following from the analysis include: (i) for small path loss exponents, MRC and minimum mean square error combining exhibit similar performance, and (ii) the gains of MRC over selection combining are smaller in the interference-limited case than in the well-studied noise-limited case.Comment: to appear in IEEE Transactions on Communication

Partial characterization of a 36-kDa antigen of Entamoeba histolytica and its recognition by sera from patients with amoebiasis

A 36-kDa antigen of axenically grown pathogenic Entamoeba histolytica (HM1-IMSS) was eluted from the sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE)-resolved crude amoebic extract antigens. The immunoreactivity of this partially purified 36-kDa antigen with monoclonal antibody (MoAb) 3D10 altered significantly (P<0.01) after heat and trypsin treatment but remained unaltered after treatment with sodium metaperiodate (P>0.5), thereby indicating the protein nature of the epitope recognized by MoAb 3D10. The epitope was found to be localized on the surface as well as in the cytoplasm of the E. histolytica trophozoites with the majority of it in the cytoplasm. In addition, this epitope was also found to be present on the cyst form of the parasite. The 36-kDa molecule was recognized by the sera from 29 (85%) of the 34 patients with amoebic liver abscess and five (83%) of the six patients with amoebic colitis. No serum samples from asymptomatic cyst passers, from patients with non-amoebic hepatic or intestinal disorders and apparently healthy subjects had antibodies that reacted with this 36-kDa molecule. The immune responses in man to this 36-kDa amoebic molecule indicate a potential specific role for this molecule in invasive amoebiasis

Annotation of mammalian primary microRNAs.

BACKGROUND: MicroRNAs (miRNAs) are important regulators of gene expression and have been implicated in development, differentiation and pathogenesis. Hundreds of miRNAs have been discovered in mammalian genomes. Approximately 50% of mammalian miRNAs are expressed from introns of protein-coding genes; the primary transcript (pri-miRNA) is therefore assumed to be the host transcript. However, very little is known about the structure of pri-miRNAs expressed from intergenic regions. Here we annotate transcript boundaries of miRNAs in human, mouse and rat genomes using various transcription features. The 5' end of the pri-miRNA is predicted from transcription start sites, CpG islands and 5' CAGE tags mapped in the upstream flanking region surrounding the precursor miRNA (pre-miRNA). The 3' end of the pri-miRNA is predicted based on the mapping of polyA signals, and supported by cDNA/EST and ditags data. The predicted pri-miRNAs are also analyzed for promoter and insulator-associated regulatory regions. RESULTS: We define sets of conserved and non-conserved human, mouse and rat pre-miRNAs using bidirectional BLAST and synteny analysis. Transcription features in their flanking regions are used to demarcate the 5' and 3' boundaries of the pri-miRNAs. The lengths and boundaries of primary transcripts are highly conserved between orthologous miRNAs. A significant fraction of pri-miRNAs have lengths between 1 and 10 kb, with very few introns. We annotate a total of 59 pri-miRNA structures, which include 82 pre-miRNAs. 36 pri-miRNAs are conserved in all 3 species. In total, 18 of the confidently annotated transcripts express more than one pre-miRNA. The upstream regions of 54% of the predicted pri-miRNAs are found to be associated with promoter and insulator regulatory sequences. CONCLUSION: Little is known about the primary transcripts of intergenic miRNAs. Using comparative data, we are able to identify the boundaries of a significant proportion of human, mouse and rat pri-miRNAs. We confidently predict the transcripts including a total of 77, 58 and 47 human, mouse and rat pre-miRNAs respectively. Our computational annotations provide a basis for subsequent experimental validation of predicted pri-miRNAs

Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods

This article describes a method developed for predicting transmembrane β-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using "leave one out cross-validation" (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane β-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred)