Twitter analysis for depression on social networks based on sentiment and stress

Detecting words that express negativity in a social media message is one step towards detecting depressive moods. To understand if a Twitter user could exhibit depression over a period of time, we applied techniques in stages to discover words that are negative in expression. Existing methods either use a single step or a data subset, whereas we applied a multi-step approach which allowed us to identify potential users and then discover the words that expressed negativity by these users. We address some Twitter specific characteristics in our research. One of which is that Twitter data can be very large, hence our desire to be able to process the data efficiently. The other is that due to its enforced character limitation, the style of writing makes interpreting and obtaining the semantic meaning of the words more challenging. Results show that the sentiment of these words can be obtained and scored efficiently as the computation on these dataset were narrowed to only these selected users. We also obtained the stress scores which correlated well with negative sentiment expressed in the content. This work shows that by first identifying users and then using methods to discover words can be a very effective technique

Towards pathway-centric cancer therapies via pharmacogenomic profiling analysis of ERK signalling pathway

Background: Genomic heterogeneity in human cancers complicates gene-centric personalized medicine. Malignant tumors often share a core group of pathways that are perturbed by diverse genetic mutations. Therefore, one possible solution to overcome the heterogeneity challenge is a shift from gene-centric to pathway-centric therapies. Pathway-centric perspectives, which underscore the need to understand key pathways and their critical properties, could address the complexity of cancer heterogeneity better than gene-centric approaches to aid cancer drug discovery and therapy. Methods: We used large-scale pharmacogenomic profiling data provided by the Cancer Genome Project of the Wellcome Trust Sanger Institute and the Cancer Cell Line Encyclopedia. In a systematic in silico investigation of ERK signalling pathway components and topological structures determines their influences on pathway activity and targeted therapies. Mann–Whitney U test was used to identify gene alterations associated with drug sensitivity with p values and Benjamini–Hochberg correction for multiple hypotheses testing. Results: The analysis demonstrated that genetic alterations were crucial to activation of effector pathway and subsequent tumorigenesis, however drug sensitivity suffered from both drug effector and non-effector pathways, which were determined by not only underlying genomic alterations, but also interplay and topological relationship of components in pathway, suggesting that the combinatorial targets of key nodes in perturbed pathways may yield better treatment outcome. Furthermore, we proposed a model to provide a more comprehensive insight and understanding of pathway-centric cancer therapies. Conclusions: Our study provides a holistic view of factors influencing drug sensitivity and sheds light on pathway-centric cancer therapies. Electronic supplementary material The online version of this article (doi:10.1186/s40169-015-0066-1) contains supplementary material, which is available to authorized users

Both low and high levels of low-density lipoprotein cholesterol are risk factors for diabetes diagnosis in Chinese adults

Aims: This study aimed to investigate whether both high and low levels of low-density lipoprotein cholesterol (LDL-C), i.e., hypercholesterolemia and hypocholesterolemia, were associated with diabetes in Chinese adults. Methods: This cross-sectional study included 22,557 Chinese adults. The LDL-C reference interval was determined from a healthy sub-cohort. Associations between hypocholesterolemia or hypercholesterolemia with diabetes were analyzed using binary logistic regression. Results: The LDL-C reference interval was 1.48–3.77 mmol/L (57.23–145.78 mg/dL). Therefore, hypocholesterolemia, normocholesterolemia, and hypercholesterolemia were defined as an LDL-C concentration of 3.77 mmol/L, respectively. Prevalence of diabetes was higher in people with hypocholesterolemia or hypercholesterolemia than that in people with normocholesterolemia. Hypocholesterolemia was associated with an increased multivariable-adjusted risk for diabetes diagnosis (odds ratio, 1.57; 95% confidence interval, 1.18–2.08), and so was hypercholesterolemia (odds ratio, 1.29; 95% confidence interval, 1.10–1.51). The results remained significant after exclusion of those who took lipid-lowering drugs from the analysis. Conclusions: This study demonstrated that both low and high levels of LDL-C were associated with a higher risk of diabetes diagnosis. Patients with either high or low LDL-C may need to be closely monitored for the risk of diabetes

Evasion of anti-growth signaling: a key step in tumorigenesis and potential target for treatment and prophylaxis by natural compounds

The evasion of anti-growth signaling is an important characteristic of cancer cells. In order to continue to proliferate, cancer cells must somehow uncouple themselves from the many signals that exist to slow down cell growth. Here, we define the anti-growth signaling process, and review several important pathways involved in growth signaling: p53, phosphatase and tensin homolog (PTEN), retinoblastoma protein (Rb), Hippo, growth differentiation factor 15 (GDF15), AT-rich interactive domain 1A (ARID1A), Notch, insulin-like growth factor (IGF), and Krüppel-like factor 5 (KLF5) pathways. Aberrations in these processes in cancer cells involve mutations and thus the suppression of genes that prevent growth, as well as mutation and activation of genes involved in driving cell growth. Using these pathways as examples, we prioritize molecular targets that might be leveraged to promote anti-growth signaling in cancer cells. Interestingly, naturally-occurring phytochemicals found in human diets (either singly or as mixtures) may promote anti-growth signaling, and do so without the potentially adverse effects associated with synthetic chemicals. We review examples of naturally-occurring phytochemicals that may be applied to prevent cancer by antagonizing growth signaling, and propose one phytochemical for each pathway. These are: epigallocatechin-3-gallate (EGCG) for the Rb pathway, luteolin for p53, curcumin for PTEN, porphyrins for Hippo, genistein for GDF15, resveratrol for ARID1A, withaferin A for Notch and diguelin for the IGF1-receptor pathway. The coordination of anti-growth signaling and natural compound studies will provide insight into the future application of these compounds in the clinical setting

Broad targeting of resistance to apoptosis in cancer

Apoptosis or programmed cell death is natural way of removing aged cells from the body. Most of the anti-cancer therapies trigger apoptosis induction and related cell death networks to eliminate malignant cells. However, in cancer, de-regulated apoptotic signaling, particularly the activation of an anti-apoptotic systems, allows cancer cells to escape this program leading to uncontrolled proliferation resulting in tumor survival, therapeutic resistance and recurrence of cancer. This resistance is a complicated phenomenon that emanates from the interactions of various molecules and signaling pathways. In this comprehensive review we discuss the various factors contributing to apoptosis resistance in cancers. The key resistance targets that are discussed include (1) Bcl-2 and Mcl-1 proteins; (2) autophagy processes; (3) necrosis and necroptosis; (4) heat shock protein signaling; (5) the proteasome pathway; (6) epigenetic mechanisms; and (7) aberrant nuclear export signaling. The shortcomings of current therapeutic modalities are highlighted and a broad spectrum strategy using approaches including (a) gossypol; (b) epigallocatechin-3-gallate; (c) UMI-77 (d) triptolide and (e) selinexor that can be used to overcome cell death resistance is presented. This review provides a roadmap for the design of successful anti-cancer strategies that overcome resistance to apoptosis for better therapeutic outcome in patients with cancer

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes

Learning about Collaborative Knowledge Building: A Case of Future School in Singapore

This study discusses the design, enactment and evaluation of a Collaborative Knowledge Building (CKB) workshop, designed to resolve the prevalent problem that Asian students tend to lack the necessary skills and appreciation for collective cognitive responsibility. The study was conducted with Secondary one (13-year-old) students in one of the future schools in Singapore. The students participated in the CKB workshop that was designed with the material and structural conditions (i.e., idea cards, knowledge wall, opportunistic grouping, reflective presentation) coupled with explicit instruction to help them learn about collaborative knowledge building skills. For evaluation, the participants completed the perception survey about collaborative learning attitudes after the workshop. We also collected and analyzed discourse data of one selected group’s discussion. The findings reveal that the students showed overall positive perception about collaborative learning experiences in the workshop and the indicators of knowledge building discourse moves in the group discussion. However, the students still needed more guidance in the process of teamwork, particularly in consensus building due to the tendency to reach a quick consensus

The effect of perceived social support on postpartum stress: the mediating roles of marital satisfaction and maternal postnatal attachment

Abstract Background Multiple factors may be responsible for the development of postpartum stress, including perceived social support, marital satisfaction, and maternal postnatal attachment. However, the underlying mediation mechanisms remain unclear. This study examined the complex relationships between perceived social support and postpartum stress among Chinese women. Methods A convenience sample comprising 406 postpartum women was recruited from six hospitals in Nantong, Jiangsu Province, China. The participants completed general survey questionnaires and were evaluated using the Maternal Postpartum Stress Scale, the Perceived Social Support Scale, the Maternal Postnatal Attachment Scale, and the Marital Satisfaction Scale. Furthermore, we evaluated the relationship between postpartum stress and the various influencing factors by performing a multiple linear regression analysis. The potential mediating roles of marital satisfaction and maternal and infant attachment in the association between perceived social support and postpartum stress were explored by performing a mediation analysis. Results According to the multivariate regression analysis, perceived social support, marital satisfaction, and maternal postnatal attachment contributed to postpartum stress levels (P < 0.05). The mediation analysis revealed that marital satisfaction and maternal postnatal attachment played parallel mediating roles in the association between perceived social support and postpartum stress, and the mediating effect of marital satisfaction was − 0.1125 (95% confidence interval [CI]: -0.1784 to -0.0520), accounting for 33.20% of the total effect, and the mediating effect of maternal postnatal attachment was − 0.0847 (95% CI: -0.1304 to -0.0438), accounting for 25.00% of the total effect. Conclusion Our study revealed that perceived social support could influence postpartum stress not only through direct effect (41.80% of the total effect), but also through the indirect effect (mediation effect) of marital satisfaction and maternal postnatal attachment (58.20% of the total effect), suggesting that improving postpartum women’s social support, enhancing maternal and infant attachment, and improving their marital satisfaction could help lower postpartum stress

Sentiment analysis for depression detection on social networks

As a response to the urgent demand of methods that help detect depression at early stage, the work presented in this paper has adopted sentiment analysis techniques to analyse users’ contributions of social network to detect potential depression. A prototype has been developed, aiming at demonstrating the mechanism of the approach and potential social effect that may be delivered. The contributions include a depressive sentiment knowledge base and an algorithm to analyse textual data for depression detection

Similarity Analysis of Learning Interests among Majors Using Complex Networks

At present, multi-specialization cross integration is the new trend for high-level personnel training and scientific and technological innovation. A similarity analysis of learning interests among specializations based on book borrowing behavior is proposed in this paper. Students of different majors that borrow the same book can be regarded as a way of measuring similar learning interests among majors. Considering the borrowing data of 75 majors, 14,600 undergraduates, and 280,000 books at the Northwest Normal University (NWNU), as an example, this study classified readers into majors depending on similarity among students. A complex network of similar learning interests among specializations was constructed using group behavior data. The characteristics of learning interests were revealed among majors through a network topology analysis, importance of network nodes, and calculation of the similarity among different majors by the Louvain algorithm. The study concluded that the major co-occurrence network was characterized as scale-free and small-world; most majors had mutual communication and an infiltrating relationship, and the 75 majors of NWNU may form six major interest groups. The conclusions of the study were related to the development of majors of the university, and a match between major learning communities was based on the borrowing interest in a similar network to reflect the relationship between the characteristics and internal operating rules of a major